2 months ago

AI/ML Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Production engineering role building Python systems that train and serve LLMs within a full-stack AI platform for supply chain intelligence. You’ll own architecture across FastAPI services, training pipelines, model inference, and deployment, with a focus on reliability, observability, and production-grade ML integration.

Location: London

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build production-grade ML-powered backends by designing and implementing the full stack from FastAPI endpoints to training pipelines and inference services
•Integrate and operationalize the domain-specific language model (DSLM) and the Project Genome infrastructure with robust data pipelines, evaluation frameworks, and deployment systems
•Collaborate with the backend and ML teams to extend FastAPI, Memgraph, and other components with ML capabilities, ensuring clean abstractions and observability
•Optimize inference performance (vLLM, quantization, batching, caching) to meet sub-200ms latency targets and ensure reliability
•Mentor teammates through code reviews and pairing, promoting code quality, testing, and production-readiness

Key Requirements

•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

Experience:5+ yearsSupply chainLogisticsAILLMsProduction engineering

Skills:PythonFastAPIPydanticAIDockerPyTorchAWSKubernetesMemgraphPostgresSQLAsync/await

Languages:English

Tech Stack:PythonFastAPIPydanticAIFastMCPMemgraphPostgresPyTorchUnslothAxolotlVLLMAWSDockerKubernetes

Company Brief

Kallikor

Kallikor builds an AI-native simulation digital-twin platform (Adaption) for supply-chain and warehouse decision-making, enabling organisations to test automation, robotics and operational changes in realistic virtual environments before deployment.

Industry: Supply Chain Technology

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Headquarters: London, United Kingdom

Founded: 2024

Glassdoor: 4.8

Website LinkedIn Glassdoor

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Kallikor

2 months ago

AI/ML Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Location: London

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build production-grade ML-powered backends by designing and implementing the full stack from FastAPI endpoints to training pipelines and inference services
•Integrate and operationalize the domain-specific language model (DSLM) and the Project Genome infrastructure with robust data pipelines, evaluation frameworks, and deployment systems
•Collaborate with the backend and ML teams to extend FastAPI, Memgraph, and other components with ML capabilities, ensuring clean abstractions and observability
•Optimize inference performance (vLLM, quantization, batching, caching) to meet sub-200ms latency targets and ensure reliability
•Mentor teammates through code reviews and pairing, promoting code quality, testing, and production-readiness

Key Requirements

•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

Experience:5+ yearsSupply chainLogisticsAILLMsProduction engineering

Skills:PythonFastAPIPydanticAIDockerPyTorchAWSKubernetesMemgraphPostgresSQLAsync/await

Languages:English

Tech Stack:PythonFastAPIPydanticAIFastMCPMemgraphPostgresPyTorchUnslothAxolotlVLLMAWSDockerKubernetes

Company Brief

Kallikor

Industry: Supply Chain Technology

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Headquarters: London, United Kingdom

Founded: 2024

Glassdoor: 4.8

Website LinkedIn Glassdoor