Teamhead – AI & Machine Learning
PUMA Open · verified Oct 22, 2026 At a glance
Mid-level Machine Learning Engineering role at PUMA. Local Office Bangalore · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Set the enterprise AI and machine-learning direction, then turn transformation priorities into a multi-year roadmap
- Lead AI, MLOps, data and application engineers, alongside data scientists, as you build and grow the team
- Guide the design and delivery of LLM, RAG, predictive-model and decision-intelligence products through deployment and ongoing improvement
- Build shared AI architectures and templates, while strengthening ML deployment, monitoring, retraining and governance practices
- Own product outcomes, including adoption, reliability, inference costs and business value, and keep security and responsible AI in view
What you bring
- A degree in computer science, engineering or a related area, plus at least twelve years across AI/ML, data platforms or enterprise product engineering
- At least five years leading technical teams across functions, with a record of taking enterprise AI/ML products into production
- Strong working knowledge of LLMs, RAG, ML governance, MLOps, modern data platforms and distributed systems
- Experience with a major cloud ecosystem and building secure, scalable enterprise data platforms while managing AI workload costs
- Fluent business English and the ability to turn technical choices into decisions for senior business stakeholders
Who this fits
This role suits a senior technical leader who can set direction, grow engineering talent and still engage closely in architecture. You’ll lead a cross-functional team and work across business and technical groups. The role is based in the local Bangalore office.
From the employer
YOUR MISSION
• Define and own the enterprise AI, ML strategy, building a multi-year roadmap aligned to digital and business transformation priorities
• Lead and scale a high-performing team of AI Engineers, MLOps Engineers, Data Engineers, Application Engineers and Data Scientists
• Architect and oversee development of enterprise-grade AI solutions including LLM-based systems, Retrieval-Augmented Generation (RAG), predictive ML models, decision intelligence platforms, and data-heavy applications
• Drive the full AI product lifecycle - opportunity discovery, business case definition, architecture design, development, deployment, monitoring, optimization and value realization
• Establish enterprise AI reference architectures, reusable frameworks, modular ML templates and standardized deployment patterns
• Ensure strong MLOps maturity including CI/CD for ML, model registry, automated retraining pipelines, monitoring for data/model drift, observability and governance
• Lead cloud-native AI platform strategy leveraging Azure / Databricks / AWS / GCP ecosystems with focus on scalability, resiliency and cost efficiency
• Define GenAI standards including prompt engineering frameworks, evaluation pipelines, guardrails, responsible AI controls and security compliance
• Partner with business leaders to translate complex operational challenges into scalable AI and data products with measurable ROI
• Define and track product KPIs including adoption, performance, latency, uptime, cost per inference, business value creation and productivity uplift
• Ensure enterprise-grade security, compliance, and responsible AI practices across all deployed solutions
• Drive continuous innovation by evaluating emerging AI technologies and integrating them into the enterprise ecosystem where strategically relevant
• Act as the single point of accountability for AI & Data product performance, delivery excellence and long-term platform scalability
YOUR TALENT
• University degree (B.Tech / M.Tech / M.S.) in Computer Science, Engineering or related discipline; MBA or equivalent business exposure is a plus
• 12+ years of experience across AI/ML, Data Platforms, or Enterprise Product Engineering, with 5+ years leading cross-functional technical teams
• Proven track record of building and scaling enterprise AI/ML products in production environments
• Deep understanding of:
Machine Learning lifecycle and model governance
LLMs, transformer architectures, RAG pipelines and prompt engineering strategies
MLOps frameworks including model monitoring, drift detection, retraining and CI/CD for ML
Modern data architectures (Databricks, Spark, ETL/ELT, Delta patterns, data orchestration)
API-driven architectures and distributed systems design
• Strong exposure to cloud ecosystems (Azure, Databricks, AWS or GCP) and cost optimization strategies for large-scale AI workloads
• Experience designing scalable, secure and highly available enterprise data platforms
• Strong strategic thinking with ability to align AI investments to business value, operational efficiency and competitive advantage
• Demonstrated experience influencing senior stakeholders and translating technical trade-offs into business decisions
• Strong leadership and talent development capability, with experience building engineering culture and technical maturity
• Excellent communication skills with ability to operate across business and technical audiences
• Hands-on mindset with ability to dive deep into architecture discussions when required
• Ability to operate in a fast-paced, high-growth and transformation-driven environment
• Fluent in Business English