GenAI Consultant

Step into a high-impact role where you’ll shape how Generative AI systems are envisioned, validated, trusted, and scaled in real-world enterprise environments. As a senior technology consultant and architect, you will partner with business, product, and engineering stakeholders to run discovery workshops, define success metrics, and create solution roadmaps for high-value GenAI and ML use cases. You’ll blend hands-on technical depth with architecture leadership—guiding teams from experimentation to production-grade systems—while designing robust testing strategies for GenAI solutions covering quality, safety, reliability, and performance.
Responsibilities
Implement Generative AI based solutions using Infosys and Industry standard tools and platforms
Implement prompt engineering techniques to evaluate model behavior under various input conditions and refining prompts for better accuracy and desired results.
Architect end-to-end Generative AI systems, including data pipelines, model selection, orchestration, evaluation, and deployment patterns using Infosys and industry-standard tools and platforms.
Design and implement ML/AI solutions using Python (or other relevant Data Science/AI languages), applying strong software engineering practices for maintainability, performance, and reliability.
Build MCP and agentic-based solutions using appropriate frameworks, enabling tool/function calling, workflow orchestration, and multi-step reasoning patterns aligned to business needs.
Test and validate GenAI applications using relevant tools and frameworks; capture, analyze, and report standard and custom quality metrics (e.g., relevance, groundedness, toxicity, latency, cost) and drive continuous improvement.
Explore and evaluate new technologies, tools, and testing methodologies to improve development processes and solution quality; stay up-to-date with advancements in Generative AI and evaluation practices.
Technical requirement (Optional)
Hands-on experience with LLM application patterns such as RAG, tool/function calling, prompt engineering, and automated evaluation frameworks.
Strong MLOps/LLMOps expertise: experiment tracking, model registry, CI/CD, observability, drift detection, and incident response for AI services.
Experience with data engineering concepts (feature stores, batch/stream processing) to support ML and GenAI workloads.
Proven ability to lead architecture governance, create reference architectures, and drive adoption across teams and portfolios.
Good to have skills
Generative AI, Automation, Devops, Python, agentic AI, Agile
Location: Pan India
Notice period: 0-60 days

Work arrangement
No