Artificial Intelligence / Machine Learning Engineer Lead (GenAI-powered applications)
FIS Global
Bengaluru, Karnataka, IndiaLEAD
HybridFIS GlobalAI
Job Description
Lead the development of AI-powered solutions within financial services.
Responsibilities
- Design and build GenAI-powered applications.
- Integrate AI systems with enterprise platforms.
Qualifications
- 10+ years of overall experience with 4+ years on AI/Gen AI/Agentic AI
- Designing and building GenAI-powered applications using LLMs (open & proprietary)
- Building agent-based systems with planning, memory, tool use, and orchestration
- Implementing Agentic workflows (multi-agent, human-in-the-loop, autonomous tasks)
- Integrating AI systems with enterprise platforms, APIs, and workflows
- Evaluating model, agent, and system behavior (accuracy, reliability, safety)
- Experience with vector databases and retrieval systems (RAG, hybrid retrieval patterns)
- Building and deploying REST services using Flask or FastAPI
- Experience working with cloud platforms (AWS, GCP, Azure) and AI Studios
- Strong hands-on experience with Large Language Models (LLMs).
- Deep understanding of Agents & Agentic AI systems—including autonomous decision‑making, tool/function calling, planning, and orchestration.
- Proficiency with prompting techniques, structured outputs, model routing, and context/memory management.
- Understanding of different model types:
- Proprietary (e.g., GPT-class)
- Open-weight models
- Embedding models
- Multimodal models
- Understanding of AI orchestration frameworks (conceptual level acceptable)
- Knowledge of model access and orchestration protocols such as Model Context Protocol (MCP) or similar abstraction layers
- Ability to design systems that:
- Switch models
- Route prompts
- Manage context, memory, and toolchains
- Experience evaluating GenAI systems (hallucinations, grounding, safety)
- Strong proficiency in Python.
- Experience with ML/AI frameworks such as:
- scikit‑learn
- TensorFlow
- PyTorch
- Keras
Nice to have
- Experience with LLM fine‑tuning, parameter‑efficient tuning (LoRA/QLoRA)
- Familiarity with LangChain, LlamaIndex, Autogen, or OpenAI Assistants API
- Working knowledge of production‑grade MLOps (CI/CD, monitoring, observability)
- Experience with vector DBs like Pinecone, Chroma, Redis, Weaviate, or Milvus
- Understanding of security, compliance, and data governance for GenAI systems
Benefits
- A multifaceted job with a high degree of responsibility and a broad spectrum of opportunities
- A broad range of professional education and personal development possibilities – FIS is your final career step!
- A competitive salary and benefits
- A variety of career development tools, resources and opportunities