At Kallikor, we're building the future of supply chain intelligence through AI-powered simulation digital twins. We create living

digital representations of real-world operations (warehouses, distribution networks, global logistics) that help organisations

make better decisions faster.

We're at an inflection point: moving from AI-assisted tools to domain-specific AI that understands supply chains as deeply as our

best engineers do. You'll be instrumental in building our first domain-specific language model (DSLM) and the foundation for

Project Genome, an ambitious initiative to capture and synthesise the world's supply chain knowledge into actionable intelligence.

This is a production engineering role first. You'll build robust Python systems that happen to train and serve LLMs, not the other

way around. We need someone who writes production-quality code, debugs complex distributed systems, and thinks about reliability,

who has learned ML/LLMs as powerful tools in their engineering arsenal.

You'll work across our entire AI stack: building FastAPI services that serve models, creating training pipelines that process

production data, deploying inference endpoints with proper monitoring, and integrating all of this into our existing Python

backend. The ML is important, but the engineering discipline is what makes it production-ready.

Learn more at kallikor.ai.

YOUR OPPORTUNITY

Build production AI systems: Design and implement the full stack, from FastAPI endpoints that handle requests, to training

pipelines that process data, to inference services that serve predictions. You'll own the architecture, not just the model

weights.

Train and deploy our DSLM: Fine-tune models using Unsloth/Axolotl, but more importantly, build the robust infrastructure around

it - data pipelines that feed training, evaluation frameworks that catch regressions, deployment systems that handle failover.

Make it production-grade.

Integrate ML into our backend: We use FastAPI, PydanticAI, FastMCP, Memgraph. You'll extend these systems with ML capabilities,

not as a separate "ML service" but as a natural part of our backend architecture. Clean abstractions, proper error handling,

observability.

Own inference performance: Get models running fast, whether that's vLLM deployment, quantization strategies, batching

optimizations, or caching. Hit our <200ms latency targets through engineering, not just throwing bigger GPUs at it.

Shape Project Genome's foundation: Work with our Principal Engineer to architect how we ingest, process, and learn from global

supply chain data. This is systems design as much as ML with data pipelines, graph databases, incremental learning strategies

being just as important.

Mentor through code review and pairing: Raise the bar on code quality, testing, and production practices across the team. Teach

mid and junior engineers how to build ML systems that don't fall over.

WHY YOU'RE MADE FOR THIS

You're a strong production Python engineer: You write clean, maintainable, tested code. You understand async/await, know when

to use generators vs lists, can profile performance bottlenecks. You've built FastAPI services (or similar) that handle

production traffic. Your code passes review without drama.

You've built with LLMs in production: You've integrated GPT-4/Claude into real applications, handled streaming responses, dealt

with rate limits and retries, cached intelligently. You know the practical challenges: prompt engineering, context management,

error handling, cost control.

You've trained or fine-tuned models: Whether it's fine-tuning LLMs, training classifiers, or running experiments, you

understand the workflow. You've dealt with training data quality, evaluation metrics, and overfitting. You can debug why a

model isn't learning what you expected.

You think like a systems engineer: You design for failure, add instrumentation, consider edge cases. You know that "the model

works on my laptop" isn't shipping. You care about monitoring, logging, alerting, and graceful degradation.

You can navigate the ML landscape pragmatically: You know enough about transformers, attention mechanisms, and training

dynamics to make informed decisions. But you're not precious about it. If a simple heuristic beats a complex model, you ship

the heuristic.

You balance velocity with quality: You ship incrementally and iterate based on production data. But you don't accumulate tech

debt, you refactor proactively, write tests that matter, and leave the codebase better than you found it.

You communicate trade-offs clearly: You can explain to the team why we're choosing LoRA over full fine-tuning, why we're

deploying on Fireworks instead of self-hosting, or why a 7B model might beat a 70B model. You help everyone make informed

decisions.

WHAT WE'RE LOOKING FOR SPECIFICALLY

Must have:

5+ years building production Python systems (backend services, APIs, data processing)

Strong software engineering fundamentals: design patterns, testing, debugging, profiling

Experience integrating LLMs into applications (OpenAI/Anthropic APIs, prompt engineering, streaming, PydanticAI)

Understanding of ML training workflows (even if you're not an expert. You need to know enough to build the infrastructure)

Docker, CI/CD, production deployment experience

Can read and understand PyTorch code (you don't need to write novel architectures)

Nice to have:

Fine-tuning experience (LoRA, full fine-tuning, QLoRA)

Distributed training basics (DeepSpeed, FSDP)

Graph databases (Memgraph, Neo4j)

Supply chain or logistics domain knowledge

Experience with agent frameworks (LangChain, PydanticAI, etc.)

WHAT YOU'LL WORK WITH

Backend Stack: Python, FastAPI, PydanticAI, FastMCP, Memgraph, Postgres

ML Stack: PyTorch, Unsloth/Axolotl for training, vLLM for inference, Weights & Biases

Models: Qwen 2.5, Llama 3.1, GPT-4, Claude (for now)

Infrastructure: AWS (flexible), Docker, Kubernetes, GPUs when needed

Team: Principal Engineer (your partner on architecture), Mid Data/ML Engineer (your data pipeline partner), Junior AI Engineer

(your mentee)

EXAMPLE PROJECTS YOU'LL OWN

Build a FastAPI service that handles streaming LLM responses with correct error handling and retry logic

Create a training pipeline that processes production logs, validates data quality, and triggers fine-tuning runs

Deploy a fine-tuned 7B model with vLLM that beats GPT-4 latency while maintaining quality on our domain

Design the data ingestion architecture for Project Genome, how we process papers, documentation, and operational data at scale

Implement evaluation frameworks that catch model regressions before they reach production

About Us

Kallikor is determined to foster an environment where people can do their best work and feel like they belong. We believe a

healthy culture, strong values and contribution from a diverse range of individuals will help us to achieve success.

We do not discriminate based on race, ethnicity, gender, ancestry, national origin, religion, sex, sexual orientation, gender

identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.

Role

AI/ML Engineer

Locations

London

Published

May 18

Deadline

August 16

Tags for this job

Python AWS Docker Kubernetes AI CI/CD

Similar Jobs

Senior AI/ML engineer

Flo Health

London•Posted: Jun 26•Deadline: Aug 16

500M+ downloads. 80M+ monthly users. A decade of building – and we’re still accelerating. Flo is the world’s #1 health & fitness app worldwide on a mission to build a better future for female health. Backed by a $200M investment led by General Atlantic, we became the first product of our kind to reach a $1B valuation in 2024 – and we’re not slowing down. With 7M paid subscribers and the highest-rated experience in the App Store’s health category, we’ve spent 10 years earning trust at scale. Now, we’re building the next generation of digital health – AI-powered, privacy-first, clinically backed – to help our users know their body better. The job We are looking for a Senior Software Engineer with deep expertise in AI/ML infrastructure to join our AI Platform team and help build the GenAI platform that powers every AI feature at Flo. You will bridge core infrastructure, data engineering, and LLM development to deliver production-grade medical safety judges, fine-tuning pipelines, evaluation frameworks, and real-time personalisation. The team operates 60+ LLM-based evaluation judges, develops proprietary fine-tuned health models, and maintains active partnerships with Databricks, Google, OpenAI, Anthropic, and AWS. What you’ll do * LLM Judge Ecosystem: build and scale Judge-as-a-Service, prompt registries, calibration pipelines, and evaluation orchestration using MLflow 3.x * Fine-Tuning and Serving: develop LoRA/SFT/preference optimisation pipelines for health-domain models (Llama, Gemma, MedGemma) and manage model serving at scale on Databricks * Data and Evaluation Pipelines: build synthetic Q&A generation, golden test sets, reward function engineering, and Delta table schemas in Unity Catalog for reliable, reproducible evaluation data * Infrastructure: maintain Terraform-managed AWS infrastructure (EKS, S3, IAM), Databricks AI Gateway, and CI/CD pipelines (GitHub Actions) with evaluation gates and progressive rollout * Cross-Functional Impact: collaborate with Product, Security, Analytics, and Medical teams, develop internal SDKs and APIs consumed by 5+ teams, and engage directly with technology partners on pre-release capabilities Experience and skills Must have: * Engineering maturity: 7+ years of software engineering, 4+ years focused on ML/AI platforms * LLM experience: recent hands-on work with at least one of: fine-tuning, prompt engineering, LLM evaluation, or model serving * Technical stack: strong Python across production services and data pipelines, data engineering fundamentals (Spark, Delta tables, Parquet) * Platform and infrastructure: Databricks (MLflow, Unity Catalog, Model Serving), AWS (EKS/Kubernetes, IAM), Terraform, GitHub Actions * Cross-domain flexibility: comfort working across ML, data engineering, and infrastructure. You don’t need to be expert in all three, but you contribute wherever the team needs it Nice to have: * LLM evaluation frameworks (judges, graders, calibration methodology) or fine-tuning techniques (LoRA, RLHF/DPO, model distillation) * ML data engineering: synthetic data generation, evaluation dataset design, annotation pipelines * Healthcare, regulated industry, or safety-critical AI systems experience * Prompt optimisation frameworks (DSPy or similar), feature stores (Tecton) #LI-KP1 #LI-Hybrid Annual Salary Range (ranges may vary based on skills and experience) £120,000—£150,000 GBP How we work We’re a mission-led, product-driven team. We move fast, stay focused and take ownership – from brief to build to impact. Debate is encouraged. Decisions are shared. We care about craft, ship with purpose, and always raise the bar. You’ll be working with people who take their work seriously, not themselves. It takes commitment, resilience, and the drive to keep going when things get tough. Because better health outcomes are worth it. What you'll get We support impact with meaningful reward. Here’s what that looks like: * Competitive salary and annual reviews * Opportunity to participate in Flo’s performance incentive scheme * Paid holiday, sick leave, and female health leave * Enhanced parental leave and pay for maternity, paternity, same-sex and adoptive parents * Accelerated professional growth through world-changing work and learning support * In-person collaboration and work in a hybrid model, with 3 days per week spent in the office * 5-week fully paid sabbatical at 5-year Floversary * Flo Premium for friends & family, plus more health, pension and wellbeing perks Diversity, equity and inclusion Our strength is in our differences. At Flo, hiring is based on merit, skill and what you bring to the role – nothing else. We’re proud to be an equal opportunity employer, and we welcome applicants from all backgrounds, communities and identities. Read our privacy notice for job applicants.

Senior Specialist Solutions Engineer (AI/ML)

Databricks

London•Posted: Jul 17•Deadline: Aug 16

ReqID: FEQ327R328 Recruiter: Kanwal Matharu Location: London, United Kingdom - Hybrid Skills: Data Science, Machine Learning, AI, LLM, GenAI As a Senior Specialist Solutions Engineer (SSE), ML Engineering, you will be the trusted technical ML expert to both Databricks customers and the Field Engineering organisation. You will work with Solution Architects to guide customers in architecting production-grade ML applications on Databricks, while aligning their technical roadmap with the evolving Databricks Data Intelligence Platform. You will continue to strengthen your technical skills through applying the latest technologies in GenAI, LLMOps, and ML, while expanding your impact through mentorship and establishing yourself as an ML expert. You will be reporting to the Manager, Field Engineering (Specialist Team) The impact you will have: * Lead the architectural design of production-grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end-to-end pipeline creation and optimization (training/inference) to seamless integration with cloud-native services. * Provide advanced technical support to the Solution Architects during the technical sales cycle by building MVPs, leading deep-dive technical sessions, and strategically aligning ML/data science solutions to complex customer business challenges using relevant real-world examples. * Serve as the trusted technical advisor for customers developing GenAI solutions, specializing in the design and implementation of RAG architectures on enterprise knowledge bases, enabling natural language querying of structured data, and establishing content generation and monitoring frameworks. * Drive community growth and platform adoption through thought leadership activities, including the creation of technical tutorials and training materials, as well as leading hackathons and presenting at industry conferences. What we look for: * Experienced, technical, customer-facing, and with a background in Data Science / Machine Learning, and Data Engineering. Looking to learn and develop in a customer-facing technical role as a subject matter expert (SME) in a pre-sales environment. * Pre-sales or post-sales experience working with external clients across a variety of industry markets Data Science/ML Skills * Hands-on industry ML experience in at least one of the following: * ML Engineer: Develop production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring * Data Scientist: Experience with the latest techniques in natural language processing, including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI * Hands-on experience working with Distributed Spark based systems. * Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience * Experience communicating and teaching technical concepts to non-technical and technical audiences alike * Passion for collaboration, life-long learning, and driving our values through ML * [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role * [Preferred] Experience working with Apache Spark™ to process large-scale distributed datasets * Can meet expectations for technical training and role-specific outcomes within 3 months of hire * Can travel up to 30% when needed About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here. Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

AI Engineer

Materiom

London•Posted: Jun 3•Deadline: Aug 16

Contract: Full-time, permanent Location: Hybrid (London office) Level: 2-4 years professional experience Salary: £75K+ depending on experience ABOUT MATERIOM Materiom is an innovation platform for regenerative materials R&D. Our mission is to accelerate the development and adoption of bio-based materials that can replace petrochemical plastics — and have a net-positive impact on the planet. We're a small, interdisciplinary team of around twelve people spanning materials science, AI/ML, software engineering, circular economy, and design. In 2026, we're at an inflection point: moving from an open, philanthropically-funded platform toward a commercial product, while keeping our public-good mission intact. Our core bet is that combining curated experimental data, domain expertise, and AI-based modelling can dramatically cut the time and cost of bio-based materials R&D. The work is genuinely novel. If that sounds like the kind of problem you want to spend your time on, read on. THE ROLE We're looking for a mid-level AI Engineer to work at the intersection of applied ML and applied AI; someone equally comfortable developing predictive models on structured scientific data as they are with building LLM-powered tools and agentic workflows. You'll work within our tech team to contribute across two primary workstreams: * Predictive modelling: developing and iterating on ML models that map bio-based formulation design spaces, and building the MLOps infrastructure to support active learning. * LLMs and agentic systems: developing and evaluating internal & external tooling that integrate Materiom’s data and model intelligence into AI agents and frontier AI product platforms. WHAT YOU'LL DO * Design, build, and deploy ML models for predicting properties of bio-based formulations from structured experimental and literature-mined data. * Develop, operate and maintain pipelines for data mining, model training, evaluation, and active learning workflows against lab equipment/partners. * Build and improve LLM-powered tools and agentic systems. * Deploy ML/AI-driven tooling to project partners, pilot users and beyond in order to gather user feedback. * Run rigorous experiments to compare modelling approaches, interpret results clearly, and iterate toward quality goals. * Contribute to LLMOps and MLOps practices — versioning, monitoring, evaluation, cost/quality optimisation. * Effectively communicate complex technical concepts and findings to multidisciplinary audiences. * Work closely within the tech team and stay closely attuned to product and scientific priorities, translating them into well-scoped technical work. WHAT WE'RE LOOKING FOR You'll need: * Master’s degree in a technical field (e.g., Computer Science, Artificial Intelligence, Machine Learning) * Around 2 to 4 years of professional experience in a data-driven or ML engineering environment. * Solid grounding in ML fundamentals — you understand what's actually happening inside your models, not just how to call the API. * Hands-on experience with structured/tabular data and real-world model development and evaluation. * Practical experience building with LLMs — prompt engineering, RAG, tool use, agentic frameworks and evaluation. * Strong Python skills and good software engineering habits: version control, testing, reproducible pipelines. * Familiarity with a major cloud platform (e.g. GCP). * Excellent problem-solving and analytical skills. * The ability to work with high agency & autonomy in an ambiguous environment. You thrive at shipping progress without needing everything defined upfront. * Experience working in an environment that translates well into a small startup setting. Useful but not required: * Proficiency in getting ML/AI-centric artefacts in front of users for obtaining real-world feedback. * Experience with active learning or closed-loop experimentation workflows. * Familiarity with scientific data from chemistry, materials science, or adjacent domains. * Experience with relevant open-source frameworks. * Background in or genuine curiosity about bio-based materials or sustainability. WHAT WE OFFER Materiom is an impact-focused startup offering a supportive and flexible environment where you can drive the acceleration of net-positive materials using cutting-edge technology. Our benefits include * Competitive Salary: An annual salary range of £75K+, commensurate with your experience and expertise. * Annual Bonuses: Eligibility for performance-based bonuses to reward your contributions to the company’s success. * Generous Paid Time Off: 30 days of paid holiday per year for full-time positions (adjusted pro-rata for part-time positions), in addition to all UK bank holidays. * Learning & Mentorship Grants: An annual individual budget dedicated to developing your hard and soft skills. * Commuter Support: Access to a Bike2Work scheme to support sustainable travel. * Flexible Hybrid Working: A highly flexible scheme that includes weekly days at the office (London) and at home, as well as options for temporary remote work. * International Retreats: Regular company retreats, often held in international locations, to build connection and celebrate progress. * Collaborative team culture: Our culture is defined by deep, interdisciplinary collaboration, offering you the exciting opportunity to work at the intersection of materials science and AI to drive positive impact for people and the planet

Materiom