AI Engineer

Tata Consultancy Services; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Pension

Health care

London Marathon sponsor

Job summary

Tata Consultancy Services is hiring an AI Engineer in London. This hands-on role focuses on building, testing and deploying production-grade AI solutions.

You will collaborate with the AI Delivery Architect, client teams and domain experts to turn designs into working software across back-end, APIs and front-end components. The role emphasizes integration, data pipelines, model endpoints, prompt management and operational reliability, with a strong focus on security, testing and scalable

Qualifications

• Hands-on delivery of LLM, retrieval or ML applications beyond tutorials or notebooks.
• Experience integrating models with enterprise applications, APIs and data sources.
• Proven ability to build, test and deploy production-grade AI software.

Responsibilities

• Convert approved requirements into technical specs, tasks and criteria.
• Build and deploy AI-enabled apps across back-end, APIs and orchestration.
• Develop AI agents with planning, tools, memory and safe failure handling.
• Create data, retrieval and context pipelines with indexing and evidence traceability.
• Integrate model endpoints, manage versioning and routing.
• Maintain data ingestion/transformation pipelines with quality checks.
• Create governed integrations using REST/gRPC, microservices and events.
• Implement authentication, secrets, retries, rate limits and audit logging.
• Write clean, testable, observable code and run comprehensive tests.
• Create evaluation data and automated tests for retrieval quality and safety.
• Instrument logs, metrics and dashboards to diagnose failures.
• Maintain deployment pipelines, IaC and rollback procedures.
• Tune reliability, cost, caching and scaling for performance.
• Contribute reusable code and documentation for AI use cases.
• Demonstrate working software and transfer knowledge to the team.

Skills

Python programming

API development

REST / gRPC

CI/CD

Docker containers

Cloud platforms

Version control

Secure configuration

Model integration

Agent frameworks

Knowledge retrieval

Debugging / troubleshooting

Education

Bachelor's or Master’s degree in Computer Science, Engineering, Data Science or related field

Tools

Docker

Kubernetes

Job description

Job Type: Permanent

Location: London

Number of hours: 40 hours per week – full time

Ready to utilize your experience and expertise in AI Engineer?

We have an exciting role for you - AI Engineer!

Careers at TCS: It means more

TCS is a purpose-led transformation company, built on belief. We do not just help businesses to transform through technology. We support them in making a meaningful difference to the people and communities they serve - our clients include some of the biggest brands in the UK and worldwide. For you, it means more to make an impact that matters, through challenging projects which demand ambitious innovation and thought leadership.

Role Summary

We are looking for a hands-on AI Engineer to build, integrate, test and deploy production-grade AI solutions. The engineer works with the AI Delivery Architect, client teams, domain teams and other delivery specialists to turn an approved design into working software. The role covers applied AI, software engineering, data and knowledge pipelines, enterprise integration, deployment automation, evaluation, observability and production troubleshooting.

The role is important because a successful AI solution must work reliably beyond a demonstration. The AI Engineer owns the practical build, proves the solution through testing and telemetry, resolves technical issues and leaves behind reusable code, documentation and operational assets that the wider team can support and extend.

Pre-requisites

• software, data, ML or platform engineering experience, including hands-on ownership of production or production-like systems
• Hands-on delivery of LLM, retrieval, agentic or ML applications beyond tutorials or isolated notebooks
• Experience integrating models and AI services with enterprise applications, APIs, data sources, workflows and systems of record
• Experience with version control, automated testing, code review, containers, CI/CD, logging, monitoring and secure configuration
• Practical experience with at least one major cloud platform and its application, data, identity, networking, monitoring and deployment services
• Ability to work directly with users, architects and engineers and take a use case from technical specification through build, test, deployment and handover

Key Responsibilities

• Work with the AI Delivery Architect, client stakeholders and domain teams to convert approved requirements into technical specifications, tasks, acceptance criteria and working increments
• Build and deploy AI-enabled applications and services across back-end, APIs, orchestration and, where required, front-end components
• Develop AI agents and sub-agents using appropriate patterns for planning, routing, tools, memory, state, delegation, human approval and safe failure handling
• Build data, retrieval and context pipelines for structured and unstructured content, including ingestion, parsing, chunking, metadata, indexing, vector or hybrid search, ranking, filtering and evidence traceability
• Integrate model endpoints and gateways, and implement prompt, model and configuration versioning, routing, fallback and environment-specific settings
• Build and maintain data ingestion and transformation pipelines with validation, data-quality checks, metadata, lineage and schema-change handling
• Build governed integrations using REST or gRPC APIs, MCP, microservices, queues, events or streams, and enterprise systems
• Implement authentication, authorization, secrets, validation, idempotency, retries, rate limits, error handling, compensation and audit logging for AI-enabled actions
• Write clean, modular, testable, observable and maintainable code, supported by unit, integration, regression, security and performance tests
• Create evaluation datasets and automated tests for retrieval quality, groundedness, task success, policy adherence, tool selection, safety, latency, cost and end-to-end outcomes
• Instrument models, retrieval, agents, tools, integrations and applications with logs, metrics, traces, dashboards and alerts; use the resulting evidence to diagnose failures
• Build and maintain deployment pipelines, containers, infrastructure configuration, release controls, rollback procedures and operational runbooks
• Tune reliability, availability, throughput, latency and cost using caching, routing, context control, scaling and deterministic fallbacks where appropriate
• Contribute reusable code, agent templates, prompt and tool contracts, connectors, evaluation assets, reference implementations and engineering documentation
• Demonstrate working software, participate in design and code reviews, support deployment and troubleshooting, and transfer knowledge to the wider team

Required Skills & Qualifications

Software & Integration Engineering

• Strong Python programming, API development and integration skills
• REST or gRPC, microservices, authentication, asynchronous or event-driven processing, databases and object storage
• Version control, automated testing, code review, CI/CD, Docker or similar containers and secure configuration
• Ability to understand unfamiliar code, isolate defects and explain implementation decisions
• Model APIs or enterprise model services, structured outputs, prompt and configuration management, tool or function calling and context handling
• Agent frameworks or equivalent orchestration approaches covering state, memory, retries, permissions and human checkpoints
• RAG and knowledge applications using embeddings, vector or hybrid search, metadata, ranking, access-aware retrieval and evaluation
• Understanding of model and agent failure modes, prompt injection, hallucination, data leakage and safe fallback patterns

Data, Platform & Production Operations

• SQL and data-engineering skills for ingestion, transformation, data quality, metadata and schema evolution
• Cloud-native application and data services on AWS, Microsoft Azure or Google Cloud
• Kubernetes or managed container services, IaC, identity, networking, secrets, logging and monitoring
• MLOps or LLMOps practices covering versioning, automated evaluation, deployment, monitoring and rollback
• Ability to troubleshoot across data, retrieval, models, prompts, orchestration, integrations, infrastructure, performance and security

Quality, Security & Professional Skills

• Creation of representative evaluation data, quality measures, thresholds, regression tests and release evidence
• Least-privilege access, masking or redaction, guardrails, audit evidence and secure tool execution
• Clear communication of progress, limitations, decisions and risks to technical and nontechnical stakeholders
• Ownership of end-to-end outcomes, with balanced attention to speed, maintainability, security, reliability and cost
• Knowledge sharing through pairing, reviews, demonstrations, documentation and reusable examples

Preferred Qualifications

• Full-stack engineering experience with React, Angular, Vue, Next.js or comparable frameworks
• Experience with streaming, distributed processing, analytical platforms, knowledge graphs or document intelligence
• Experience in regulated, security-sensitive or high-availability environments
• Experience with conversational or voice AI, contact-center integration or multimodal applications
• Relevant cloud developer, AI engineer, data engineer, DevOps, Kubernetes or application-security certification
• Bachelor’s or master’s degree in Computer Science, Engineering, Data Science or a related field, or equivalent professional experience

What Success Looks Like

• The engineer converts the approved design into stable, testable and observable working software
• Data, retrieval, model, integration and deployment components work together reliably in the target environment
• Evaluation and telemetry provide evidence for release decisions and ongoing improvement
• Reusable code and documentation reduce effort and risk for subsequent AI use cases
• The wider team can independently build, deploy, troubleshoot and extend the solution after the incubation phase

TCS is consistently voted a Top Employer in the UK and globally. Our competitive salary packages feature pension, health care, life assurance, laptop, phone, access to extensive training resources and discounts within the larger Tata network.

We offer health & wellness initiatives and sports events; we are the proud sponsor of the London Marathon.

Diversity, Inclusion and Wellbeing

Tata Consultancy Services UK&I is committed to meeting the accessibility needs of all individuals in accordance with the UK Equality Act 2010 and the UK Human Rights Act 1998.

We welcome and embrace diversity in race, nationality, ethnicity, disability, neurodiversity, gender identity, age, physical ability, gender reassignment, sexual orientation. We are a disability inclusive employer and encourage disabled people to apply for this role.

As a Disability Confident Employer, we offer an interview to applicants with disabilities or long-term conditions who meet the minimum criteria for the role. Please email us at [e-mail] if you would like to opt in.

If you are an applicant who needs any adjustments to the application process or interview, please contact us at [e-mail] with the subject line: “Adjustment Request” or call TCS London Office 02031552100 / [phone] to request an adjustment. We welcome requests prior to you completing the application and at any stage of the recruitment process.

Due to the high volume of applications, we will be unable to contact each applicant individually on the status of their application. If you have not received a direct response within 30 days, then it should be deemed unsuccessful on this occasion.