The company is seeking to lead the design and evolution of large-scale ML and LLM systems, build resilient ML infrastructure, and grow its ML practice.
Requirements
- Deep expertise in ML infrastructure, including training pipelines, model lifecycle management, and monitoring.
- Proven success in deploying production-grade ML systems at scale.
- Hands-on experience with AI, LLMs, and data engineering.
- Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow).
- Experience with data engineering and cloud platforms (AWS, GCP, Azure, Snowflake, Spark, Airflow).
- Familiarity with containerization and orchestration (Docker, Kubernetes).
- Demonstrated success in presales or consulting engagements, including building client relationships and delivering technical proposals.
Responsibilities
- Define and drive the architectural vision for ML and LLM systems that power personalization, intelligent recommendations, and real-time decision-making.
- Lead the development of reliable, scalable ML infrastructure for training, inference, monitoring, and lifecycle management.
- Establish foundational design patterns and best practices for ML observability, testing, and performance.
- Build and maintain scalable, secure data and ML infrastructure to support advanced use cases.
- Architect robust pipelines for training, evaluation, deployment, and monitoring of ML and LLM models.
- Partner with internal delivery, engineering teams, and clients to translate complex problems into scalable ML solutions.
- Lead architecture design discussions with C-level client stakeholders, balancing scalability, cost, and performance.
Other
- 7–10+ years of experience in Machine Learning Engineering, with 3+ years in technical leadership.
- Mentor senior and lead engineers, fostering a culture of system ownership, clarity, and innovation.
- Engage with clients and prospects in presales cycles to understand their needs, design tailored ML/AI and Data solutions, and demonstrate technical feasibility.
- Collaborate with sales and business development teams to create proposals, proofs of concept, and technical presentations.
- Act as a trusted advisor by consulting with clients on best practices for Data/ML adoption, infrastructure strategy.
