Data Engineer

Job Description AI EngineerLocation

Delhi / Mumbai

(Work from office)

Experience

5+ years of hands-on experience in AI / ML engineering and data systems.

About the Role

We are looking for a seasoned AI Engineer who can design, build, and scale intelligent systems end-to-end. This role requires strong depth in machine learning, cloud platforms (GCP & AWS), and large-scale ETL data pipelines . You will work closely with product, data, and engineering teams to convert data into production-grade AI solutions.

Key Responsibilities

AI & Machine Learning

• Design, train, fine-tune, and deploy machine learning, deep learning, and generative AI models .
• Work on NLP, embeddings, recommendation systems, and multimodal AI use cases.
• Build and maintain RAG pipelines , similarity engines, and ranking models.
• Translate business and product requirements into scalable AI architectures.

Data Engineering & ETL Pipelines

• Design and build robust ETL pipelines for high-volume data ingestion and processing.
• Handle structured and unstructured data including events, text, audio, video, and metadata.
• Ensure data reliability, quality checks, and monitoring across pipelines.
• Create ML-ready datasets, feature stores, and analytics layers.
• Optimize pipelines for cost, latency, and scalability .

Cloud & Infrastructure (GCP + AWS)

• Architect and deploy AI and data workloads on Google Cloud Platform (GCP) and Amazon Web Services (AWS) .
• Hands-on experience with:
• GCP : BigQuery, Dataflow, Pub/Sub, Vertex AI, Cloud Storage, TerraForm, BigQuery, Python Scripting
• AWS : S3, EC2, Lambda, SageMaker, Glue, Redshift
• Manage training and inference workloads using CPU and GPU infrastructure.
• Implement secure, scalable, and fault-tolerant cloud architectures.

MLOps & Production Systems

• Build and maintain end-to-end MLOps pipelines for model training, deployment, and monitoring.
• Use tools such as MLflow, Kubeflow, Airflow, Weights & Biases .
• Containerize models using Docker and orchestrate with Kubernetes .
• Monitor model drift, performance, and retraining cycles in production.

Collaboration & Ownership

• Work closely with product, backend, and analytics teams.
• Communicate AI insights and system designs to technical and non-technical stakeholders.
• Own AI systems from design through production rollout.

Required Skills & Technologies

Programming & Frameworks

• Strong proficiency in Python .
• ML frameworks: PyTorch, TensorFlow, Hugging Face .
• API development: FastAPI, REST, gRPC .

Data & Pipelines

• ETL orchestration: Apache Airflow, Dataflow, AWS Glue .
• Streaming systems: Kafka, Pub/Sub .
• Databases: SQL, NoSQL (PostgreSQL, BigQuery, MongoDB).
• Vector databases: Pinecone, FAISS, Weaviate .

Cloud & DevOps

• Deep production experience with GCP and AWS .
• Infrastructure as Code: Terraform or CloudFormation (preferred).
• CI/CD pipelines and Git-based workflows.

Nice to Have

• Experience with media-tech, content platforms, or consumer-scale products .
• Exposure to LLMs, generative AI, and personalization systems .
• Experience handling millions of events per day .

Why Join Us

• Work on real-world AI systems with clear business impact.
• High ownership role in a fast-growing product-led environment.
• Opportunity to architect AI and data systems from the ground up.
• Competitive compensation and long-term growth opportunities.