Senior Gen AI Developer

Birlasoft Limited; Telangana; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Birlasoft Limited is seeking a seasoned GenAI Senior Lead to drive end-to-end GenAI/ML initiatives across cloud deployments, preferably on GCP. The role focuses on model development, microservice architectures, and production readiness in a collaborative, cross-functional setting.

Location options include Noida, Pune, Bengaluru, HYD, and Mumbai, with a strong emphasis on SDLC practices, cost awareness, and governance. Immediate notice is expected.

Qualifications

• 10-12 yrs overall experience with 5-7 yrs in GenAI/ML model development and cloud deployment.
• Strong knowledge of GCP and AI/ML offerings like Vertex AI and BigQuery.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Experience designing microservice architectures and containerized deployments (Docker, Kubernetes).
• Experience in MLOps: versioning, experiments, monitoring, governance.
• Data processing, SQL, ETL/ELT, and data visualization.

Responsibilities

• Lead end-to-end GenAI/ML model development from framing to deployment.
• Architect microservice AI solutions and deploy in containerized environments (GKE).
• Leverage GCP tools to design scalable AI data workflows.
• Implement RAG concepts and manage production models with observability.
• Collaborate with cross-functional teams to translate business needs into ML solutions.
• Mentor junior scientists and contribute to code reviews and standards.

Skills

Python

GCP

Vertex AI

Docker

Kubernetes

REST/GraphQL

MLOps

Data pipelines

BigQuery

Pytorch

Tools

GKE

Docker

Kubernetes

Job description

GEN AI Sr Lead Area(s) of responsibility JD – Senior Developer Gen AI ( 5B Grade) Location- Noida/Pune/Bengaluru/HYD/Mumbai Exp-7-11 Years Notice- Immediate

Summary: We are seeking seasoned Senior developer GenAI with overall 10-12 yrs and at least 5-7 years of hands‑on experience in developing GenAI/machine learning models and deploying them in a cloud environment, preferably on Google Cloud Platform (GCP). The ideal candidate will design microservice‑based solutions, containerize deployments (e.g., GKE), and drive end‑to‑end SDLC practices. Experience in the pharma domain is a strong advantage.

Key Responsibilities

• Lead end‑to‑end development of GenAI/ML models: problem framing, data preparation, model selection, training, evaluation, and iteration.
• Architect and implement microservice‑based AI solutions and deploy them in containerized environments (preferably GKE); define APIs and data contracts.
• Leverage GCP offerings (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE, etc.) to design scalable AI solutions and efficient data workflows.
• Knowledge of Retrieval‑Augmented Generation (RAG) concepts and processes.
• Proficiency with Google Cloud Platform (GCP) and its AI/ML offerings (e.g., Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE).
• Deploy, monitor, and maintain models in production; implement observability (logs, metrics, tracing), cost optimization, and performance tuning.
• Collaborate with cross‑functional teams (data engineers, software engineers, product, regulatory/compliance, analytics) to translate business needs into robust ML solutions.
• Uphold SDLC standards: requirements gathering, design, development, testing, deployment, maintenance, and documentation; promote reusable patterns and best practices.
• Mentor and guide junior scientists; contribute to code reviews, standards, and knowledge sharing.
• Stay current with GenAI advancements and evaluate new tools/approaches; produce reproducible experiments and artifacts.

Required Qualifications

• Overall 10-12yrs and Minimum 5-7 years of hands‑on experience developing GenAI/ML models and deploying them in a cloud environment.
• Proficiency with Google Cloud Platform (GCP) and its AI/ML offerings (e.g., Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE).
• Must have experience working with any agentic framework Knowledge of Retrieval‑Augmented Generation (RAG) concepts and processes
• Strong software engineering skills: Python (primary), experience with ML frameworks (TensorFlow, PyTorch, scikit‑learn), and API development (REST/GraphQL).
• Experience designing and deploying microservices architectures and containerized solutions (Docker, Kubernetes; preference for GKE).
• Solid experience in MLOps: model versioning, experiments, automated training, feature stores, model registries, monitoring, and governance.
• Data processing and analytics expertise: SQL, data pipelines, ETL/ELT concepts, data quality, and data visualization support.
• Excellent problem‑solving, communication, and collaboration skills; ability to work with cross‑disciplinary teams.
• Understanding of cloud security concepts, IAM, and basic principles of data privacy and compliance.
• Demonstrated ability to translate business problems into scalable ML solutions and to communicate technical concepts to non‑technical stakeholders.

Experience Level Senior Level