AI/ML Engineer Tech Leads

AI/ML Technical Lead

Role Summary

The AI/ML Technical Lead is responsible for leading the design, development, and deployment of machine learning and AI-driven solutions. This role involves overseeing a team of data scientists, ML engineers, and software developers, ensuring technical excellence, scalability, and business impact. The Technical Lead bridges research, engineering, and product teams to deliver end-to-end intelligent systems that solve complex business problems.

Key Responsibilities

1. Technical Leadership

• Lead the end-to-end lifecycle of AI/ML projects—from problem definition and data exploration to model deployment and monitoring.
• Architect and design scalable machine learning pipelines and production-ready systems.
• Evaluate and select appropriate ML frameworks, libraries, and infrastructure (e.g., TensorFlow, PyTorch, AWS Sagemaker, Azure ML, GCP Vertex AI).
• Review code, model performance, and solution design to maintain high-quality standards.
• Drive best practices in model versioning, experiment tracking, and CI/CD for ML (MLOps).

2. Team Leadership & Collaboration

• Mentor and guide data scientists, ML engineers, and junior developers.
• Foster a collaborative, innovative environment focused on continuous learning and technical excellence.
• Collaborate cross-functionally with product managers, data engineers, and business stakeholders to define and prioritize AI initiatives.
• Translate business objectives into AI/ML technical solutions and actionable deliverables.

3. Research & Innovation\

• Stay current with emerging AI trends, tools, and methodologies (e.g., GenAI, LLMs, multimodal models, reinforcement learning).
• Prototype new techniques to improve performance, interpretability, and scalability of models.
• Evaluate academic research and transform it into practical, production-grade implementations.

4. Delivery & Operations

• Oversee project timelines, deliverables, and resource allocation.
• Ensure robustness, security, and compliance of deployed AI systems.
• Establish monitoring and feedback mechanisms to ensure continuous model performance in production.
• Manage risks, data governance, and ethical AI considerations.

Required Qualifications\

• Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics, or related field. PhD preferred for research-intensive roles.

Experience:

• 6–10+ years of experience in software engineering, data science, or ML engineering.
• 2–4 years of experience leading AI/ML teams or projects.

Technical Skills:

• Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.)
• Deep understanding of supervised/unsupervised learning, NLP, computer vision, or time series forecasting.
• Strong grasp of data structures, algorithms, and software design principles.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, Prefect).
• Hands-on experience with APIs, microservices, and deployment pipelines.

Soft Skills:

• Excellent communication skills for both technical and non-technical audiences.
• Strong problem-solving, analytical, and decision-making abilities.
• Leadership, mentoring, and stakeholder management skills

Preferred Qualifications

• Experience with large language models (LLMs), generative AI, or prompt engineering.
• Experience integrating AI systems with enterprise products or SaaS platforms.
• Background in data governance, model explainability (XAI), and responsible AI practices.
• Contributions to open-source AI/ML projects or publications in reputed conferences/journals.

Key Performance Indicators (KPIs)

• Quality and accuracy of deployed models (e.g., precision, recall, ROI impact).
• Efficiency of AI delivery pipelines and model deployment frequency.
• Team skill growth and retention.
• Business value generated from AI solutions.
• Reduction in time-to-market for AI/ML initiatives.