Hands-on technical leader in the AI CoE to architect, build, integrate, and run production-grade AI solutions (Core
ML + GenAI + Agentic) across multiple initiatives. You will technically manage/mentor junior AI/ML engineers, engage stakeholders/clients, and ensure solutions meet enterprise-grade standards (security, reliability, governance).
What You’ll Do
1. Architect & deliver end-to-end AI-enabled software solutions: Core ML/DL, GenAI (LLMs, RAG), and agentic workflows, integrated into existing products.
2. Lead engineering execution across multiple projects: design reviews, code reviews, technical decisions, and mentoring junior engineers.
3. Enterprise integration: build AI capabilities into legacy/enterprise apps using APIs, microservices, event-driven patterns, queues/streams, and secure integration approaches.
4. Stakeholder & client engagement: define AI use cases with business/external stakeholders; lead PoCs; contribute to solution proposals / RFPs .
5. MLOps/DevOps ownership: CI/CD for apps + models, containerization, release automation, model registry, monitoring, and rollback strategies.
6. Process & governance: deliver under Agile/SAFe/Waterfall, produce HLD/LLD, and follow compliance, data governance, and change-control processes.
7. CoE initiatives: build reusable components, reference architectures, internal libraries, standards, and accelerators.
What You Bring (Must-Have)
1. Strong software engineering fundamentals: Python + solid backend engineering; ability to integrate with UI layers (web apps) as needed.
2. Hands-on AI/ML delivery: model development + deployment experience across traditional ML and deep learning, plus GenAI patterns (RAG, prompt engineering, evals; fine-tuning is a plus).
3. Experience building agentic / tool-using LLM systems (or equivalent orchestration patterns) in real implementations.
4. Cloud proficiency in one: AWS or Azure or GCP (compute, storage, networking, security; managed ML services a plus).
5. DevOps/MLOps: Docker, Kubernetes, CI/CD; ML lifecycle tooling such as MLflow/Kubeflow/SageMaker/Azure ML (or equivalent).
6. Working knowledge of enterprise data platforms / ETL pipelines.
7. Exposure to enterprise monitoring/ticketing (e.g., Splunk/Datadog/AppDynamics; Jira/ServiceNow) or similar operational toolchains.
8.Experience working in secure/regulatory environments and adhering to data governance(PII,access controls,auditability).
Python, Backend Engineering, Core Machine Learning, Deep Learning, Generative AI, Large Language Models, Retrieval Augmented Generation, Prompt Engineering, Model Evaluation, Fine Tuning, Agentic Workflows, LLM Orchestration, API Integration, Microservices Architecture, Event Driven Architecture, Secure Integration, AWS, Azure, GCP, Docker, Kubernetes, CI/CD, MLOps, MLflow, Kubeflow, SageMaker, Azure ML, Enterprise Data Platforms, ETL Pipelines, Splunk, Datadog, AppDynamics, Jira, ServiceNow, Data Governance, PII Handling, Access Controls, Auditability, Agile, SAFe, Waterfall, Solution Architecture, High Level Design, Low Level Design, Stakeholder Management, Technical Mentoring