Job Description

Key Responsibilities

• Lead the development and deployment of advanced data science and AI solutions across Machine Learning (ML), Deep Learning (DL), Generative AI, and Agentic AI use cases
• Lead the end-to-end model lifecycle management, including data ingestion, feature engineering, model development, evaluation, deployment, monitoring, and retraining
• Lead the development and operationalization of Generative AI and Agentic AI systems, including prompt engineering, orchestration frameworks, tool integration, and autonomous agent workflows
• Collaborate with cross-functional teams (Engineering, Data Engineering, Product, QA, DevOps) to deliver robust AI-powered solutions
• Provide technical leadership and mentorship to data scientists and ML engineers, driving capability development and code quality standards
• Ensure AI solutions comply with organizational governance frameworks, including security, privacy, ethical AI, and regulatory requirements
• Evaluate and integrate modern AI/ML tools, frameworks, and platforms (e.g., model serving frameworks, vector databases, orchestration tools)
• Translate complex business problems into scalable AI solutions and communicate outcomes effectively to stakeholders and leadership

Person Specifications

• Bachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field
• 6–8 years of professional experience in Data Science, AI, or ML, working in production-grade environments

Technical Expertise

• Strong hands-on experience in Machine Learning and Deep Learning (supervised/unsupervised learning, NLP, computer vision)
• Practical experience in Generative AI (LLMs, prompt engineering, RAG pipelines, embeddings, fine-tuning)
• Experience designing and implementing Agentic AI systems (multiagent orchestration, tool usage, autonomous workflows)
• Solid knowledge of MLOps practices (model lifecycle management, CI/CD, monitoring, retraining pipelines)
• Experience in LLMOps (prompt/version management, evaluation frameworks, guardrails, cost optimization, observability)
• Proficiency in Python and common AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Experience with cloud platforms (AWS, Azure, or GCP) and cloudnative platforms & services (e.g., Copilot Studio, Bedrock, Vertex AI, Azure OpenAI)
• Familiarity with data engineering concepts (ETL pipelines, feature stores, data lakes/warehouses)

Leadership & Soft Skills

• Strong architectural thinking and problem-solving skills with the ability to design scalable AI systems
• Proven ability to lead teams, mentor engineers, and drive delivery in fast-paced environments
• Excellent communication skills with the ability to engage both technical and non-technical stakeholders
• Experience working within governance frameworks (e.g., AI governance, security, compliance)
• Strong ownership mindset with a focus on quality, performance, and business impact