GEN AI Sr Lead

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

Job summary

Birlasoft is seeking a Senior Developer Gen AI with 10-12 years of experience, including 5-7 years hands-on GenAI/ML development and cloud deployments. The role focuses on designing scalable GenAI solutions, deploying on GKE/Docker, and collaborating across data, software, and regulatory teams.

The candidate will utilize Vertex AI and other GCP tools to build, train, and productionize models with strong SDLC discipline and observability.

Qualifications

• Overall 10–12 years in AI/ML roles with 5–7 years hands-on GenAI experience.
• Proficiency with Google Cloud Platform (GCP) and AI offerings (Vertex AI, BigQuery, Dataflow, Cloud Run, GKE).
• Experience with agentic frameworks and production-grade ML deployments.
• Strong software engineering skills: Python, ML frameworks, API development (REST/GraphQL).
• Knowledge of MLOps, data pipelines, and SDLC practices for GenAI solutions.

Responsibilities

• Lead end-to-end development of GenAI/ML models: framing, data prep, training, evaluation.
• Architect microservice-based AI solutions and deploy in containerized environments (GKE/Docker).
• Leverage GCP offerings to design scalable AI solutions and data workflows.
• Implement RAG concepts and ensure observability, cost optimization, and performance tuning.
• Collaborate with data engineers, software engineers, and product/regulatory teams.
• Mentor junior scientists and contribute to code reviews and best practices.

Skills

GenAI

ML models

Python

GCP

GKE

Docker

Kubernetes

Data pipelines

SQL

REST/GraphQL

MLOps

RAG

Vertex AI

Tools

Docker

Kubernetes

GKE

Job description

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, 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.