Senior AI/ML Engineer

Job SummaryDesign, develop, deploy, and support enterprise AI solutions leveraging Machine Learning, Generative AI, and Agentic AI. The role focuses on delivering scalable, secure, and governed AI systems while ensuring operational excellence, compliance, and responsible AI practices across the entire AI lifecycle.Responsibilities AI & ML Engineering · Develop, deploy, and optimize machine learning and deep learning models. · Build AI solutions for NLP, computer vision, predictive analytics, and intelligent automation. · Integrate AI services into enterprise applications and internal platforms. Generative AI · Develop LLM-based applications, AI copilots, and RAG solutions. · Implement prompt engineering, model evaluation, embeddings, and semantic search. · Optimize model performance, scalability, and cost. Agentic AI · Design and deploy autonomous and multi-agent AI systems. · Develop agent orchestration, planning, reasoning, memory, and tool integration. · Implement human-in-the-loop approvals and enterprise workflow automation. AI Operations (MLOps / LLMOps / AgentOps) · Build CI/CD pipelines for AI models and agents. · Monitor AI systems for performance, drift, hallucinations, security, and reliability. · Manage model versioning, deployment, observability, and lifecycle operations. AI Governance & Responsible AI · Implement AI governance, model risk management, and Responsible AI practices. · Maintain model documentation, validation, audit readiness, and compliance. · Ensure fairness, traceability, maintainability, privacy, and regulatory adherence. Agent Governance & Security · Govern AI agent lifecycle, identity, access, and runtime controls. · Implement guardrails, audit logging, and secure integration with enterprise systems. · Protect AI solutions against prompt injection, data leakage, and adversarial attacks. Collaboration · Work with business, engineering, security, and architecture teams to deliver AI solutions. · Mentor team members and promote AI engineering standards and best practiceTechnical SkillsAI & Machine Learning · Machine Learning, Deep Learning, NLP, Computer Vision · Predictive Analytics, Feature Engineering, Model Optimization Generative AI · Large Language Models (LLMs) · Retrieval-Augmented Generation (RAG) · Prompt Engineering · Embeddings and Semantic Search · Fine-tuning Foundation Models Agentic AI · Autonomous and Multi-Agent Systems · Agent Orchestration · Planning and Reasoning · Memory Management · Tool Calling · Human-in-the-Loop (HITL) · Model Context Protocol (MCP) Frameworks & Libraries · PyTorch · TensorFlow · Scikit-learn · Hugging Face · LangChain · LangGraph · LlamaIndex · AutoGen · CrewAI · Semantic Kernel Cloud & Infrastructure · Microsoft Azure AI Foundry · Azure Machine Learning · AWS Bedrock · Amazon SageMaker · Google Vertex AI · Docker · Kubernetes · TerraformData & Integration · SQL · PostgreSQL · MongoDB · Apache Spark · Vector Databases · REST APIs · FastAPI AI Operations · MLflow · Kubeflow · Azure ML Pipelines · GitHub Actions · Azure DevOps · AI Monitoring & ObservabilityEducational Qualifications and Certifications· Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, DataScience, Information Technology, Software Engineering, or a related field.· Master's degree in AI, Machine Learning, Data Science, or Computer Science is preferred.· Relevant experience and continuous professional development in AI technologies will beconsidered an advantage.· Artificial Intelligence & Machine Learning: TensorFlow Developer, NVIDIA AICertifications, Databricks Machine Learning, or equivalent AI/ML certifications.· Generative AI & Agentic AI: Microsoft Applied Skills (Azure OpenAI), OpenAI, Anthropic,Google Gemini, LangChain, or equivalent Generative AI/LLM certifications, with hands-onexperience using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel,Mastra, LlamaIndex, or similar platforms.· MLOps & AI Engineering: Kubernetes (CKA/CKAD), Docker, MLflow, Kubeflow, CI/CD,AI observability, and production AI operations certifications.· Security, Governance & Compliance: Responsible AI, AI Governance, AI Security, DataPrivacy, Cybersecurity (e.g., CISSP, CCSP), Model Risk Management, or equivalentgovernance and compliance certifications.Work ExperienceMinimum:· Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.· 5+ years of software or AI/ML engineering experience.· 3+ years building and operating production AI solutions.· Strong Python programming and software engineering skills.· Experience with enterprise AI governance, cloud platforms, and secure AI development.