Key Responsibilities:
- Hands-on Solution Architecture: Architect, design, develop, and deploy end-to-end, production-grade AI/ML and data analytics solutions on GCP.
This includes writing code for critical modules and frameworks. - Technical Implementation: Lead the implementation of complex projects, including building scalable data pipelines, developing and optimizing ML models, and ensuring the technical integrity of the final solution.
- Technical Mentorship: Act as the lead technical mentor for the AI & Data team.
Conduct code reviews, establish best practices in software engineering and MLOps, and guide engineers in solving complex technical challenges. - Client Technical Advisory: Serve as the primary technical expert during client engagements.
Directly interface with client-side architects and engineers to design and troubleshoot complex systems for national and international clients. - Prototyping & Innovation: Lead proof-of-concept (PoC) development and rapid prototyping to demonstrate the feasibility of new AI solutions and technologies for enterprise-scale problems.
- Performance Optimization: Identify and resolve performance bottlenecks in data processing, model training, and inference to ensure solutions are highly optimized and efficient.
Must-have Skills & Qualifications:
- 5+ years of demonstrable hands-on experience designing, building, and deploying mission-critical AI, Machine Learning, and Data Analytics systems.
- Expert-level proficiency in Python and its core data science libraries (e.G., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
- Deep, hands-on expertise with the Google Cloud Platform (GCP) with proven experience using services like Vertex AI, BigQuery, Google Kubernetes Engine (GKE), Dataflow, and Pub/Sub in production environments.
- Must have a background in the technology services or consulting industry with a portfolio of successfully delivered projects.
- Significant hands-on project experience within the Banking, Financial Services, and Insurance (BFSI) sector is mandatory.
- Proven ability to lead technical delivery for large-scale enterprise projects while remaining deeply involved in the implementation details.
- Strong practical experience with Agile methodologies, CI/CD pipelines, and MLOps principles.
- A true player-coach mindset with a passion for building great technology and mentoring others to do the same.
Good-To-Have (Preferred):
- Hands-on experience with the NVIDIA technology stack (e.G., CUDA, cuDNN, Triton Inference Server) is a significant plus.
- Experience with containerization and orchestration technologies like Docker and Kubernetes.
- Contributions to open-source projects in the AI/ML space.
Why Join us?
- Build and lead tech solutions that power cutting-edge AI platforms.
- Work in a cloud-native product environment with full lifecycle ownership.
- Be part of a cross-functional innovation lab solving meaningful problems.
- Competitive salary, flexible work culture, and leadership growth opportunities.
- Exposure to enterprise clients, modern architecture, and AI-first development.