Lead AI Engineer

To be successful in this role, you should have below skills:

• Bachelor’s degree (or above) in Computer Science, Engineering, Mathematics, or related discipline
• 8+ years software engineering experience with at least 3 years building AI/ML systems, scalable ML infrastructure, or model serving platforms at enterprise scale
• Strong proficiency in Python (FastAPI, asyncio) and/or Go/Java, with production experience building APIs and microservices serving high-throughput, low-latency workloads
• Hands-on experience developing agentic AI systems and LLM applications including multi-agent orchestration, tool use, planning frameworks, and deployment of multi-step AI workflows
• Solid technical knowledge of prompt engineering, RAG architectures, and modern agentic frameworks (Strands, LangGraph, Google ADK) with ability to build and optimise agent-based solutions
• Working experience with GenAI/LLM systems prompt engineering, retrieval-augmented generation, model serving (vLLM), and OpenAI-compatible API surfaces
• Strong understanding of distributed systems architecture — microservices, cloud platforms, API design (REST, gRPC, OpenAPI), and event-driven architectures
• Experience with AWS services — EC2/EKS, S3, IAM, RDS, and at least one AI service (Bedrock, SageMaker)
• Hands-on experience with Docker and Kubernetes — containerising applications and deploying to orchestrated environments
• Experience with CI/CD pipelines, Infrastructure-as-Code (CloudFormation, Terraform), and observability tools (OpenTelemetry, distributed tracing)
• Ability to work on system design from ideation through completion with limited supervision; effective communication with both technical and non-technical stakeholders

Some other highly valued skills may include below:

• Experience building evaluation frameworks for agentic systems including agent benchmarking, reasoning traces, and monitoring observability for multi-step AI workflows in production
• Experience in vector databases, knowledge graphs, semantic search, and memory systems for stateful agents with understanding of retrieval optimisation and context management
• Experience implementing AI safety controls — content filtering, guardrails, prompt injection detection, PII redaction
• Knowledge of AI security frameworks — NIST AI RMF, MITRE ATLAS, OWASP LLM Top 10
• Familiarity with model fine-tuning techniques (LoRA, RLHF, supervised fine-tuning) for domain-specific SLMs
• Knowledge of responsible AI practices, model governance, and security considerations for autonomous systems
• Understanding of Zero Trust architecture and identity systems (EntraID, OAuth2/OIDC)
• Background in financial services — understanding of regulatory requirements, data residency, and compliance obligations
• Experience with GPU workload management and inference optimisation (vLLM)

You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills.

This role is based in Bengaluru and Pune office.

Purpose of the role

To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues.

Accountabilities

• Development and delivery of high-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance.
• Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives.
• Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing.
• Stay informed of industry technology trends and innovations and actively contribute to the organization’s technology communities to foster a culture of technical excellence and growth.
• Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions.
• Implementation of effective unit testing practices to ensure proper code design, readability, and reliability.