- Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors.

- Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services).

- Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup.

- Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment.

- Make high-level design decisions on model architecture and data pipelines.

- Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects.

- Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions.

- Present findings and ML model results to non-technical audiences in a clear manner, and refine solutions based on their feedback.

- Quality, Security & Compliance: Ensure that ML solutions meet quality and performance standards.

- Implement monitoring and logging for models in production, and proactively improve model accuracy and efficiency.

- Given the sensitive nature of our data, enforce data security best practices and compliance with relevant regulations (e. data privacy and confidentiality) in all ML workflows.

Required Qualifications & Experience :

- Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or related field. Strong foundation in statistics and algorithms is expected.

- Experience: 5+ years of hands-on experience in machine learning or data science roles, with a track record of building and deploying ML models into production. Prior experience leading projects or teams is a plus for a lead role.

- Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch).

- Solid understanding of ML algorithms, model evaluation techniques, and optimisation.

- Experience with NLP techniques, generative AI or financial data modelling is advantageous.

- Cloud & DevOps: Proven experience with AWS cloud services relevant to data science particularly Amazon SageMaker for model development and deployment.

- Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena/Redshift, etc.) is expected. Strong knowledge of DevOps/MLOps practices

- candidates should have built or worked with CI/CD pipelines for ML, using tools like Docker and Jenkins, and infrastructure-as-code tools like CloudFormation or Terraform to automate deployments.

- Hybrid Work Skills: Ability to thrive in a hybrid work environment

- should be self-motivated and communicative when working remotely, and effective at in-person collaboration during on-site days. (The role will be based in Chennai with a mix of remote and office work.

- Soft Skills: Excellent problem-solving and analytical thinking.

- Strong communication skills to explain complex ML concepts to clients or management.

- Ability to work under tight deadlines and multitask across projects for different clients.

- A client-focused mindset is essential, as the role involves understanding and addressing the needs of large clients who come to us because they trust us.