Lead AI strategy and implement GenAI, agentic AI, and automation to modernize legacy systems, integrate AI into engineering lifecycle, and build production-grade, scalable AI solutions with cloud, containerization, MLOps, and data pipeline best practices.
This person would help to drive the AI strategy for solving business problems, implement AI, GenAI, Agentic AI for automation, legacy software modernization, engg lifecycle integration and for productivity enhancements and tooling.
Required Qualifications
- 8+ years of professional experience in AI, machine learning, or software engineering with AI focus
- Deep expertise in at least one of: Generative AI, LLMs, agentic AI systems, or modern AI frameworks
- Strong programming skills in Python, Java, Go, or similar languages
- Demonstrated experience building and deploying production-grade AI systems
- Proficiency with popular AI/ML frameworks (TensorFlow, PyTorch, LangChain, AutoGen, etc.)
- Experience with cloud platforms (AWS, Azure, Google Cloud) and containerization (Docker, Kubernetes)
- Understanding of software architecture, design patterns, and enterprise system integration
- Knowledge of data engineering, data pipelines, and relevant data governance practices
- Familiarity with DevOps, MLOps, and CI/CD practices
- AI/ML Technologies: Deep learning, NLP, computer vision, reinforcement learning, foundation models
- GenAI Tools: ChatGPT, Claude, Gemini, open-source models; API integration and deployment
- Agentic AI: Agent frameworks, multi-step reasoning, tool integration, agent orchestration
- Software Architecture: Microservices, serverless computing, distributed systems, system design
- Data Engineering: Data pipelines, ETL processes, data quality, feature engineering
- DevOps & MLOps: Model versioning, experiment tracking, monitoring, deployment automation
- Analytics & Evaluation: Metrics definition, model evaluation, performance benchmarking
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