Senior AI Engineer

Senior AI / Machine Learning Engineer (LLMs, MLOps, Generative AI, Cloud Deployment)

Location:
Pune

Experience:
7+ Years (AI, Machine Learning, Deep Learning & MLOps)

About the Role

We are seeking a
Senior AI & Machine Learning Engineer
with proven experience in building and deploying advanced AI systems at scale. The ideal candidate will have deep expertise in
Large Language Models (LLMs)
,
Generative AI
,
MLOps
, and
cloud-based AI infrastructure
. You'll lead the end-to-end lifecycle of AI model development — from research and architecture design to production deployment and optimization — driving innovation in intelligent automation, RAG systems, and multi-agent AI frameworks.

This is a
strategic and hands-on role
for someone passionate about shaping the future of enterprise AI solutions.

Key Responsibilities
AI / ML Development & Innovation

• Lead the design, development, and deployment of
  complex ML and deep learning models
  for NLP, computer vision, and multimodal AI applications.
• Work extensively with
  transformer architectures
  (GPT, LLaMA, Claude, T5, Gemini, etc.) for pre-training, fine-tuning, and inference optimization.
• Architect
  Retrieval-Augmented Generation (RAG)
  systems integrating vector databases (Pinecone, FAISS, Weaviate) with LLMs for contextual and domain-aware intelligence.
• Develop and scale
  LangChain-based
  applications and
  multi-agent AI systems
  enabling autonomous reasoning and orchestration.
• Drive
  prompt engineering and optimization
  strategies to enhance accuracy, efficiency, and token cost optimization.
• Implement
  model retraining pipelines, hyperparameter tuning
  , and
  continuous learning
  frameworks for performance improvement.
• Establish
  modular and reusable AI architectures
  enabling rapid experimentation and scalability across projects.

MLOps & Production Deployment

• Architect and manage
  end-to-end MLOps pipelines
  using MLflow, Kubeflow, Airflow, or Vertex AI for continuous integration, deployment, and monitoring.
• Automate the full AI lifecycle — from data ingestion and preprocessing to training, validation, CI/CD, and production rollout.
• Collaborate closely with
  Data Engineering and DevOps
  teams to ensure scalable, secure, and seamless cloud deployments (AWS, Azure, GCP).
• Build high-performance
  inference APIs
  using FastAPI, Flask, or TensorFlow Serving.
• Implement
  real-time monitoring
  ,
  model drift detection
  , and
  automated rollback
  systems to maintain production reliability.
• Manage
  feature stores (Feast, Delta Lake)
  and
  data versioning systems (DVC, MLflow)
  to ensure reproducibility and governance.

Cloud Architecture & Infrastructure

• Design and maintain
  cloud-native ML environments
  using AWS Sagemaker, Azure ML, or GCP Vertex AI.
• Use
  Docker, Kubernetes, and Terraform
  for containerized, scalable, and secure deployments.
• Optimize
  GPU/TPU utilization
  for distributed model training and large-scale inference workloads.
• Leverage
  serverless ML architectures
  (Lambda, Cloud Run) and
  multi-cloud strategies
  for resilience and cost efficiency.
• Integrate
  observability and performance monitoring
  using Prometheus, Grafana, and Evidently AI dashboards.

Required Expertise

• 7+ years of hands-on experience in
  Machine Learning, Deep Learning, and AI system deployment
  .
• Strong understanding of
  LLMs, NLP, and Generative AI
  concepts and frameworks.
• Proficiency with
  TensorFlow, PyTorch, scikit-learn, LangChain, and Hugging Face Transformers
  .
• In-depth knowledge of
  MLOps tools
  — MLflow, Airflow, Kubeflow, or Vertex AI.
• Experience deploying
  enterprise-scale AI applications
  across
  cloud environments (AWS / Azure / GCP)
  .
• Familiarity with
  vector databases, prompt engineering, and RAG systems
  .
• Excellent knowledge of
  data pipelines, CI/CD, and container orchestration
  .

Preferred Qualifications

• Master's or Ph.D. in
  Computer Science, Data Science, or Artificial Intelligence
  .
• Prior experience leading
  AI architecture, R&D initiatives
  , or
  multi-agent LLM systems
  .
• Strong track record of delivering
  production-grade AI solutions
  for SaaS, fintech, or automation-driven companies.
• Contributions to
  open-source projects
  or
  research publications
  in AI/ML fields.

Soft Skills

• Strategic thinker with the ability to translate research into scalable business solutions.
• Excellent leadership, communication, and cross-functional collaboration skills.
• Passion for mentoring junior engineers and driving AI innovation culture.