Responsibilities :
AI Architecture & Engineering
Define and own AI reference architectures for generative AI, agentic systems, and AI augmented applications
Architect scalable solutions using LLMs, multi agent systems, orchestration frameworks, and AI pipelines
Design AI platforms supporting model serving, prompt management, RAG, and workflow orchestration
Establish architectural standards for performance, scalability, reliability, and cost efficiency Platform Engineering & Integration
Build reusable AI components for LLM integration, vector search, embeddings, and inference services
Enable secure and scalable deployment using Kubernetes, serverless platforms, and CI/CD pipelines
Integrate AI capabilities into enterprise systems using APIs, SDKs, and event driven architectures
Collaborate with QE teams to embed AI into test automation, test data generation, and intelligent validation Engineering Governance & Quality
Define architectural guardrails for model lifecycle, versioning, monitoring, and rollback
Ensure adherence to non functional requirements including performance, observability, and fault tolerance
Leverage observability tools to monitor model performance and drift
Review designs and implementations for architectural compliance and code quality
Mentor engineers and architects on AI engineering best practices Core Platforms, Frameworks & Tooling
LLM and foundation model platforms (e.g., AWS Bedrock, Azure OpenAI, Vertex AI)
Agentic AI and orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, Google ADK or equivalent)
Vector databases and search technologies (OpenSearch, Pinecone, FAISS, Weaviate)
Model lifecycle and deployment tooling (Kubernetes, containers, serverless runtimes)
CI/CD and MLOps tooling for AI pipelines (GitHub Actions, Azure DevOps, Jenkins)
Observability and monitoring tooling for AI systems (OpenTelemetry, Prometheus, Grafana) Client Orientation & Leadership
Partner with product and engineering teams to identify AI opportunities and shape roadmaps
Support client workshops, RFPs, and solution presentations
Mentor engineers on AI/ML/Gen AI best practices and emerging technologies
Translate complex AI concepts into business-friendly narratives.
Technical and Professional Requirements:
13+ years of experience in software engineering with 3+ years in AI with strong architecture ownership
Proven experience designing and implementing enterprise-scale AI engineering or MLOps platforms
Strong hands on experience with LLMs, prompt engineering, RAG, and agent frameworks
Proficiency in Python, AI frameworks, and cloud-native AI services
Experience in Kubernetes, CI/CD, and secure deployment of AI models
Experience integrating AI capabilities into enterprise scale systems Good to Have Skills
Experience with multi agent orchestration and autonomous workflows
Knowledge of model observability and monitoring tooling
Exposure to QE platforms, test automation frameworks, or AI assisted testing
Domain experience in regulated industries such as BFSI, Healthcare, Telecom
Cloud and AI certifications
