Skills and Qualifications:
- Strong hands-on experience in Machine Learning, Artificial Intelligence, and Generative AI development.
- Proven experience implementing AI/ML solutions in production environments.
- Hands-on experience working with Large Language Models (LLMs) and Generative AI applications.
- Experience developing agentic AI solutions and multi-step AI workflows.
- Strong programming skills in Python, including development of APIs, services, and ML/AI pipelines.
- Retrieval-Augmented Generation (RAG)
- Prompt engineering
- Tool/function calling
- AI agents
- Model orchestration
- Embeddings and vector search
- LLM evaluation and monitoring
- Exposure to frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, Hugging Face, or similar.
- Experience working with REST APIs, microservices, data pipelines, and enterprise integration patterns.
- Understanding of MLOps/LLMOps, CI/CD pipelines, automated testing, version control, deployment, and model monitoring.
- Experience with Git, Docker, Kubernetes, Jenkins, GitHub Actions, Azure DevOps, or comparable SDLC technologies.
- Knowledge of cloud-based AI/ML platforms such as Microsoft Azure/Azure AI, AWS/SageMaker/Bedrock, or Google Cloud/Vertex AI.
- Experience designing AI/ML solutions within model governance, model risk management, security, privacy, and regulatory frameworks.
- Strong analytical and troubleshooting skills for supporting models and AI applications in production.
- Strong communication and stakeholder-management skills, with the ability to work directly with internal clients to collect requirements and translate them into technical solutions.
- Experience supporting release planning, implementation, production deployments, and post-release activities.
- Design, develop, orchestrate, and implement production-grade Machine Learning and Generative AI solutions.
- Build applications and services leveraging LLMs, Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI frameworks.
- Develop and integrate AI agents and workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent technologies.
- Evaluate and integrate foundation models and enterprise AI platforms based on business, security, performance, and governance requirements.
- Implement SDLC tooling and engineering harnesses to improve developer productivity, testing, deployment consistency, and overall team efficiency.
- Partner with internal clients and business stakeholders to elicit, analyze, and translate requirements into technical designs and implementation plans.
- Communicate technical requirements, solution designs, dependencies, and delivery expectations across engineering and business teams.
- Develop model pipelines covering data preparation, model integration, orchestration, testing, evaluation, deployment, monitoring, and ongoing support.
- Design solutions with model governance, responsible AI, risk management, security, auditability, and regulatory compliance incorporated throughout the development lifecycle.
- Support AI/ML models and services deployed within production applications, including troubleshooting, performance monitoring, and continuous improvement.
- Establish appropriate LL M evaluation and monitoring mechanisms, including model quality, hallucination risk, latency, reliability, and operational performance.
- Participate in release planning, release coordination, deployment activities, and hands-on production support during release windows.
- Collaborate with engineering, architecture, infrastructure, security, data, risk, and governance teams to ensure solutions meet enterprise standards.
- Maintain technical documentation covering solution architecture, model behavior, dependencies, deployment procedures, testing, and operational support.