Role : Lead Machine Learning Engineer Generative AI

Location : Baner, Pune (Hybrid)

Shift Timing : 2 : 00 PM 11 : 00 PM IST

Notice Period : Immediate 15 Days

Experience : 5+ Years

About the Position :

We are seeking a Lead Machine Learning Engineer who can spearhead the development and implementation of advanced Generative AI and LLM-driven solutions. This role will oversee a team of ML Engineers and Data Scientists, delivering scalable AI systems and driving business value through cutting-edge machine learning, NLP, and generative modeling capabilities.

Key Responsibilities :

- Provide leadership and mentorship to a team of ML Engineers and Data Scientists, fostering a culture of innovation and technical growth.

- Architect, design, and roll out applications leveraging LLMs and Generative AI technologies.

- Own ML initiatives end-to-end, covering research, system design, deployment, and ongoing optimization.

- Develop scalable machine learning pipelines and production-ready workflows following MLOps best practices.

- Work closely with engineering, product, and business stakeholders to align ML strategy with organizational objectives.

- Craft robust system architectures that seamlessly integrate AI components with enterprise

and cloud ecosystems.

- Maintain high-quality standards for code, technical documentation, and reproducibility.

- Promote continuous learning, experimentation, and knowledge sharing across the ML team.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a

related discipline.

- 5+ years of practical experience in AI/ML with a proven track record of deploying production-grade solutions.

- Strong command of Python and its scientific ecosystem (NumPy, Pandas, Scikit-learn).

- Expertise with leading ML/DL frameworks such as PyTorch or TensorFlow.

- Solid understanding of NLP, LLM architectures, and Generative AI methodologies.

- Experience in building APIs using frameworks like Flask, FastAPI, or Django.

- Proficiency with cloud platforms (AWS, GCP, or Azure) and hands-on deployment experience.

- Working knowledge of SQL/NoSQL databases, version control (Git), CI/CD systems, and

containerization (Docker).

- Strong grounding in software engineering practices and MLOps workflows.

Preferred Qualifications :

- Experience with Big Data ecosystems and tools such as Apache Spark, Kafka, or Kinesis.

- Familiarity with cloud-native ML services (AWS SageMaker, GCP Vertex AI, Azure ML).

- Understanding of data visualization platforms like Tableau or Power BI.