Digital S​/W Eng Lead Analyst -Vice President

The Digital Software Engineering Lead Analyst is a strategic technical leader responsible for designing and engineering enterprise grade Agentic AI solutions capable of integrating data from multiple heterogeneous systems and operating reliably at scale.

You will act as a hands‑on architect, engineer, and partner to cross functional teams—including Data Engineering, Architecture, Enterprise Platforms, and Product—defining the technical approach, AI system design, and integration patterns needed to build robust fault‑tolerant AI agents and AI‑driven automation capabilities.

This role requires deep technical breadth across machine learning, LLMs, data pipelines, cloud engineering, orchestration, and modern AI frameworks. The solutions you design will enable strategic automation, cognitive decisioning, and dynamic multi‑agent workflows across the organization.

• Design and build agentic AI systems, including autonomous agents, multiagent orchestration, tool use, and adaptive decision‑making workflows.
• Architect fault tolerant, scalable AI solutions using modern agent frameworks (e.g., , Lang Graph, Lang Chain, OpenAI Assistants, CrewAI, Auto Gen, custom orchestrators).
• Define the end‑to‑end AI system blueprint, including knowledge integration, orchestration, pipelines, observability, governance, and failover strategies.
• Evaluate and select LLMs, embeddings, vector stores, and middleware best suited for complex enterprise requirements.

• Partner with engineering teams to aggregate, ingest, and harmonize data from multiple systems, including APIs, databases, internal platforms, and unstructured sources.
• Design robust data pipelines optimized for LLM workloads (e.g., chunking, metadata design, semantic indexing, retrieval strategies).
• Implement mechanisms for ensuring data freshness, quality, and fault tolerance across distributed systems.

• Build advanced Retrieval‑Augmented Generation (RAG) architectures, including hybrid retrieval, query planning, and retrieval optimization.
• Develop, tune, and deploy applications leveraging major LLMs (OpenAI, Gemini, Claude, Llama, Mistral, Hugging Face ecosystem).
• Engineer prompts, system instructions, and reusable prompt templates for deterministic AI behavior.
• Implement safety guardrails, evaluation pipelines, and bias/error mitigation strategies.

• Develop cloud native GenAI applications using containerized infrastructure (Kubernetes, Open Shift, Docker).
• Build and support production‑grade MLOps / AIOps pipelines, including CI/CD, automated testing, monitoring, model versioning, and rollback strategies.
• Partner with engineering teams to ensure secure, compliant deployment of all AI workloads.

• Serve as technical SME for AI engineering patterns, solution design, and architecture.
• Mentor mid‑level engineers and analysts, guiding best practices in AI build patterns and engineering quality.
• Influence product and platform strategy by providing insights on emerging GenAI and agentic technologies.

• 10+ years of experience in software engineering, AI/ML engineering, systems architecture, or related fields.
• Proven experience designing and deploying enterprise‑grade AI Systems in production.

Core AI/ML & GenAI Expertise

• Strong foundations in ML, NLP, embeddings, statistics, neural networks, and LLMs.
• Extensive hands‑on experience with LLMs:
  Gemini, OpenAI, Claude, Mistral, Llama, open‑source models, etc.
• Deep expertise in RAG architectures, including retrieval optimization, vector search, and semantic data modeling.
• Experience building agentic AI using  or lang Graph.

Programming & Data Engineering

• Strong proficiency in Python and libraries such as:
  Pandas, Num Py, scikitlearn, PyTorch, Tensor Flow, Transformers, FastAPI, Lang Chain, Llama Index.
• Hands‑on experience with vector databases:
  Pinecone, PGVector, Mongo
  
  DB Atlas Vector Search, Neo4j, Milvus, etc.
• Experience building pipelines for large‑scale unstructured data processing.

Cloud, Dev Ops,…