Generative AI Engineer Full Time

Key Responsibilities

Engineering Production Grade Development (40%)

• Handson Coding: Lead the development of production ready systems with expert level proficiency in Python or PySpark.
• Solution Engineering: Apply advanced data structures, algorithms, and software design patterns to solve real-world financial services challenges.
• Scalability Performance: Architect high scale solutions with a big picture approach, ensuring system latency and infrastructure costs are optimized to drive tangible business value.
• Lifecycle Management: Own the transition from MVP to live solutions, managing change control and enterprise-level AI integration.

Advanced AI Implementation & RD (40%)

• Generative AI RAG: Design and deploy sophisticated RAG pipelines and Transformer-based architecture. Orchestrate LLM as a Service (e.g., GPT4, Gemini, Vertex AI) alongside local SLMs (e.g., Llama 3, Mixtral).
• Agentic Workflows: Build autonomous agents using frameworks such as LangGraph (preferred), CrewAI, or AutoGen to automate complex multistep business logic.
• MLOps Governance: Take full ownership of the model lifecycle, including finetuning LLM/SLM, monitoring model drift, groundtruth validation, and ensuring compliance with Model Risk Management (MRM) standards.
• UAT Validation: Drive User Acceptance Testing (UAT) focused on specific business outcomes and accuracy benchmarks.

Leadership & Solution Orchestration (20%)

• Stakeholder Management: Navigate complex client ecosystems, acting as the primary technical liaison for both C-suite stakeholders and engineering teams.
• Strategic Communication: Translate high-level business requirements into sustainable, high-value generative AI solutions.
• CoE Liaison: Partner with the BFS AI CoE to bring cutting-edge RD and incubated solutions. Active participation for multiple client AI solutions demo and delivery assurance to global clients.
• Team Mentorship: Lead and inspire a high-performing technical team, fostering a positive solution mindset and an initiative-taking culture.

Technical Qualifications

• Education: Bachelors or Masters degree in Computer Science, Engineering, or a related quantitative field.
• Experience: 10 years Enterprise Application development. Proven track record of delivering AIML solutions in a production environment, specifically within the BFSI or highly regulated sectors.
• Tooling: Deep expertise in Python, LangGraph/LangChain, Vector Databases, and cloud-native AI stacks (Azure AI, AWS Bedrock, or GCP Vertex AI).