AI Engineer
Mid-level AI Engineer role at Capco (Wipro) focused on developing Generative AI solutions for financial services, requiring expertise in LLM deployment, prompt optimization, and integration with banking/insurance systems to drive industry transformation.
Skills required
About this role
Job Title: GenAI Developer/ Engineer
About Us
“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.
WHY JOIN CAPCO?
You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.
MAKE AN IMPACT
Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.
#BEYOURSELFATWORK
Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.
CAREER ADVANCEMENT
With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.
DIVERSITY & INCLUSION
We believe that diversity of people and perspective gives us a competitive advantage.
Location - Bangalore, Pune, Hyderabad, Chennai
Experience - 5 to 10
Skills and Qualifications:
Experience: around 5 years in software engineering, with recent hands-on experience in AI/LLM systems, strong OOP and dynamic programming skills
Stack:
Core: AutoGen, Pydantic AI, Python, Langfuse, Mem0 + Redis, RAG pipeline (vector search + retrieval), Embedding models
Optional: Pipecat, LangChain, ChromaDB
Scope:
Design and orchestrate multi-agent systems using AutoGen & Pydantic AI
Implement RAG pipelines, memory management, and observability with Langfuse
Integrate security, performance, and quality gates into production systems
Prompt & Context engineering incl. memory management.
Guardrails:
Implement policy enforcement for prompt injection prevention
Add role-based access control (RBAC) for agent actions
Validate tool invocation safety and restrict sensitive operations
Apply content filtering for PII and compliance-sensitive data
Security & Compliance: OAuth2/OIDC, JWT, secrets management (Azure Key Vault/HashiCorp Vault), PII redaction
Quality/Evals: Writing & monitoring Eval scores, A/B experiments; Langfuse metrics as quality gates
Performance: Async workers (Celery/RQ), batching, Redis caching strategies
Observability: OpenTelemetry integration; dashboards for latency, throughput, error rates, token/cost
