Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Build and deploy innovative AI agents across various domains.
- Company: Join a pioneering tech company focused on AI transformation.
- Benefits: Competitive pay, growth opportunities, and a high-impact work environment.
- Why this job: Shape the future of AI with real-world applications and high autonomy.
- Qualifications: 4-7 years in AI/ML engineering and experience deploying production AI agents.
- Other info: Collaborate with founders and leaders in a dynamic, fast-paced setting.
The predicted salary is between 60000 - 84000 £ per year.
We are looking for an experienced AI Engineer who specializes in building agents and agentic systems—from task-orchestration agents to workflow automation agents, retrieval-augmented agents, research/coding agents, multimodal agents, and domain-specific autonomous agents. This is a full-stack AI engineering role, ideal for someone who loves shipping: rapid MVPs → stable production, high ownership, and fast problem-solving. Candidates must have built and deployed at least two AI agents in production in the past 12 months and be comfortable operating in high-velocity environments.
What You'll Do
- Build & Deploy AI Agents
- Design, build, and ship agentic workflows across multiple domains (research agents, coding assistants, conversational agents (voice, texts, etc), reasoning agents, scheduling agents, analytics agents, workflow automation bots, etc.).
- Own the end-to-end lifecycle: data ingestion → reasoning → action taking → evaluation → monitoring.
- Build multi-step agents capable of autonomous planning, context tracking, memory, tool use, and API orchestration.
- Architect systems using modern agent stacks (LangChain, LlamaIndex, OpenAI Assistants, Model Context Protocol (MCP), custom orchestration).
- Build robust retrieval pipelines (RAG), vector embeddings, caching layers, and knowledge-grounding systems.
- Integrate agents with external tools and systems (APIs, SaaS apps, CRMs, internal services, databases, messaging platforms).
- Deploy agents as microservices with proper observability, evals, guardrails, fallbacks, and monitoring.
- Optimize inference cost, latency, accuracy, and task-completion rates.
- Run systematic evaluations: function calling accuracy, groundedness, hallucinations, long-context stability.
- Work closely with product managers, domain experts, and engineers to translate business workflows into agent behaviours.
- Create reusable frameworks and libraries to accelerate subsequent agent builds.
- Document and evangelize agent best practices internally.
What You Bring
- Required:
- 4–7 years of hands-on experience in AI/ML engineering.
- Successful deployment of at least two production AI agents in the past 12 months (not prototypes).
- Expertise in:
- LLMs: OpenAI, Anthropic, Gemini, Llama, DeepSeek
- Agent frameworks: LangChain, OpenAI Assistants, custom orchestration, state machines
- Retrieval (RAG), vector DBs (Pinecone, Weaviate, Chroma, PGVector)
- API integration & tool-use architectures
- Python/Node for server-side agent logic
- Microservice deployments (Docker, Kubernetes, CI/CD)
- Strong debugging skills across distributed systems, prompt engineering, inference optimization, and agent reasoning traces.
- Comfortable building MVPs in days and scaling them to stable production within weeks/months.
- Experience building MCP servers or integrating with MCP tools.
- Experience with structured function-calling workflows (JSON schema, tool plans, agent graphs).
- Background in building internal agent frameworks or automation engines.
- Experience designing evaluation frameworks for agents (task completion metrics, scenario tests).
- Familiarity with workflow engines (Temporal, Airflow, Prefect).
Success Looks Like
- In your first 3–6 months, you will:
- Build and deploy multiple agents that solve real business workflows.
- Improve accuracy, response quality, and reliability of existing agents.
- Establish a reusable internal agent framework to increase build velocity.
- Contribute significantly to cost, latency, and performance improvements.
- Become a core owner of agentic architecture and experimentation.
Why Join Us
- Work directly with founders and senior leaders driving AI-first transformation.
- Build real agents used daily — not research prototypes.
- High autonomy + high impact environment.
- Opportunity to shape the foundation of agentic systems across the org.
- Competitive compensation + massive growth opportunity.
Senior AI Engineer in London employer: Vikara AI
Contact Detail:
Vikara AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior AI Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI space, attend meetups, and join online communities. The more connections we make, the better our chances of landing that Senior AI Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing the AI agents you've built and deployed. We want to see your work in action, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of agent frameworks and productionisation techniques. We need to be ready to discuss our experience with LLMs and microservices confidently.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about joining our team.
We think you need these skills to ace Senior AI Engineer in London
Some tips for your application 🫡
Show Off Your Experience: Make sure to highlight your hands-on experience with AI agents in your application. We want to see those two production agents you’ve built in the last year, so don’t hold back on the details!
Tailor Your Application: Take a moment to customise your application for this role. Use the job description as a guide and align your skills and experiences with what we’re looking for. It shows us you’re genuinely interested!
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate straightforward communication, so avoid fluff and focus on what makes you a great fit for the Senior AI Engineer role.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Vikara AI
✨Know Your Agents Inside Out
Make sure you can discuss the AI agents you've built in detail. Be ready to explain the architecture, the challenges you faced, and how you overcame them. This shows your hands-on experience and problem-solving skills.
✨Showcase Your Full-Stack Skills
Prepare to talk about your experience with the entire lifecycle of AI agents—from data ingestion to deployment. Highlight specific tools and frameworks you've used, like LangChain or OpenAI Assistants, to demonstrate your expertise.
✨Be Ready for Technical Questions
Expect questions on debugging distributed systems and inference optimisation. Brush up on your knowledge of microservices, Docker, and Kubernetes, as these are crucial for the role. Practice explaining complex concepts in a simple way.
✨Demonstrate Collaboration Skills
Since you'll be working closely with product managers and engineers, prepare examples of how you've successfully collaborated in the past. Discuss how you translated business needs into technical solutions, showcasing your ability to bridge the gap between teams.
