Lead AI Engineer

TestYantra Software Solutions; Warwick; On-site; GBP 90,000 - 120,000; Full time

Job summary

A technology solutions firm in the UK is seeking a Lead AI Engineer to oversee the lifecycle of GenAI and agentic systems. You will drive the engineering standards across projects, focusing on end-to-end delivery from prototyping to production. The ideal candidate has over 12 years of software engineering experience, with a solid background in AI/ML technologies. Responsibilities include designing workflows, architecting solutions, and ensuring compliance with security standards. This role offers a dynamic environment with opportunities for technical leadership.

Benefits

Competitive salary

Flexible working hours

Professional development opportunities

Qualifications

• 12+ years of software engineering experience with 3+ years in AI/ML/GenAI.
• Strong system design and scalable architecture skills.
• Hands-on expertise with LLM orchestration frameworks.
• Proven experience with RAG and vector databases.
• Proficiency in Python and at least one of JavaScript/TypeScript or Java.

Responsibilities

• Own the full lifecycle of GenAI and agentic systems.
• Design and implement workflows with LLM orchestration tools.
• Architect robust RAG pipelines with vector databases.
• Build scalable services and APIs in Python/JS/Java.
• Enforce encryption and security compliance in delivery.

Skills

AI/ML/GenAI

System design

Python

Cloud (AWS/GCP/Azure)

Tools

LangChain

Docker

Kubernetes

MLflow

Spark

Job description

About the Role

We’re looking for a Lead AI Engineer to own the delivery of GenAI and agentic systems end-to-end—from research prototypes to secure, observable, scalable production systems. You will set engineering standards across AI projects, lead technical design, guide LLM orchestration, and drive platform reliability, performance, and cost efficiency.

Responsibilities

• End-to-end delivery: Own the full lifecycle (discovery → prototyping → hardening → production → monitoring → continuous improvement) of GenAI and agentic systems.
• LLM orchestration & tooling: Design and implement workflows using LangChain, LangGraph, LlamaIndex, Semantic Kernel or similar. Optimize prompt strategies, memory, tools, and policies.
• RAG & vector search: Architect robust RAG pipelines with vector DBs (Pinecone, Chroma, Weaviate, pgvector), including chunking, hybrid search, embeddings selection, caching, and evaluation.
• Guardrails & observability: Implement policy/guardrails, safety filters, prompt/content validation, and LLMOps observability (tracing, token/cost monitoring, drift detection, eval harnesses).
• Architecture & microservices: Build scalable services and APIs in Python/JS/Java; define contracts, SLAs, and resiliency patterns (circuit breakers, retries, idempotency).
• Cloud & platform engineering: Design for AWS/GCP/Azure using managed services; containerize with Docker, orchestrate with Kubernetes, and automate via CI/CD.
• Security-first delivery: Enforce encryption, secrets management, IAM/least-privilege, privacy-by-design, data minimization, and model compliance requirements.
• MLOps & model serving: Operationalize models via MLflow/SageMaker/Vertex, with feature/data/version management, model registry, canary/blue-green rollouts, and rollback plans.
• Data engineering: Build reliable data pipelines (batch/stream) using Spark/Airflow/Beam; ensure data quality, lineage, and governance.
• Technical leadership: Lead design reviews, mentor engineers, enforce coding standards, documentation, and SRE best practices. Partner with Product, Security, and Compliance.
• Performance & cost: Optimize latency, throughput, token usage, context windows, and hosting strategies; manage budgets and efficiency.

Qualifications

• 12+ years overall software engineering experience with 3+ years hands-on in AI/ML/GenAI, including production deployments.
• Strong system design and scalable architecture skills for AI-first applications and platforms.
• Hands-on expertise with LLM orchestration frameworks (e.g., LangChain/LangGraph/LlamaIndex/Semantic Kernel).
• Proven experience with RAG and vector databases (e.g., Pinecone, Chroma, Weaviate, pgvector).
• Proficiency in Python (primary) and at least one of JavaScript/TypeScript or Java.
• Solid foundation in cloud (AWS/GCP/Azure), Docker/Kubernetes, and CI/CD.
• Practical knowledge of guardrails, prompt/context engineering, multimodal workflows, and observability.
• Experience with MLOps/model serving (e.g., MLflow, SageMaker, Vertex AI) and data pipelines (e.g., Spark, Airflow, Beam).
• Security-first mindset and familiarity with compliance (PII handling, RBAC/IAM, key management).

Nice-to-Have

• Experience with function/tool calling, agent frameworks, and structured output (JSON/JSON Schema).
• Knowledge of embedding models, rerankers, hybrid search (BM25 + vector), and evaluation frameworks.
• Exposure to cost/latency trade-offs across hosted vs. self-hosted models; GPU inference (Triton, vLLM, TGI).
• Familiarity with feature stores, streaming (Kafka/PubSub), and data contracts.
• Domain experience in [your industry/domain—e.g., BFSI, healthcare, manufacturing].
• Contributions to OSS, publications, patents, or speaking at AI/ML conferences.