AI Engineer

LOCATION : Bengaluru, India

EXPERIENCE : 3 to 5 years

OPENINGS : 2

EMPLOYMENT TYPE : Full-time

About BDB :

BDB (Big Data BizViz) is a unified, low-code data, analytics, and decision-intelligence platform. The BDB Platform brings data pipelines, a medallion lakehouse, a governed semantic layer, data products, and agentic AI analytics together in a single product used by enterprise and government customers across telecom, BFSI, manufacturing, and the public sector. Our global R&D hub is in Bengaluru, and the platform is engineered to run mission-critical, high-volume workloads under an ISO 27001 : 2022-certified operating model.

Role Overview :

As an AI Engineer, you will build and operationalise the machine-learning and generative-AI backbone of the BDB Platform from training, deployment, and monitoring pipelines to the agentic, semantic-layer-grounded AI (BDB Data Agents) that powers conversational analytics with grounded, zero-hallucination answers. You will work closely with data scientists (BDB DS Lab), platform, and product teams to take models and AI features from notebook to reliable, scalable production.

Key Responsibilities :

- Build and maintain end-to-end AIOps pipelines training, validation, deployment, monitoring, and automated retraining with reproducibility and versioning across data, code, and models.

- Deploy and serve ML models at scale (batch and real-time), containerised and orchestrated on Kubernetes.

- Engineer GenAI / LLM capabilities RAG pipelines, embeddings, vector search, and prompt / agent orchestration grounded in BDBs Kinetic Semantic Layer for accurate, governed answers.

- Contribute to BDB Data Agents : agentic, conversational analytics grounded in the semantic layer and business ontology.

- Build feature-engineering and feature-store workflows, integrated with the platforms data pipelines and lakehouse.

- Implement model and LLM monitoring drift, performance, data quality, cost, and guardrails / evaluation.

- Optimise inference for cost and latency (GPU / CPU sizing, quantisation, caching, batching).

- Collaborate with data scientists, data engineers, and product to productionise models and AI features securely and reliably.

Must-Have Skills & Experience :

- 3-5 years in MLOps / ML Engineering / AI Engineering.

- Python & engineering strong Python with solid software-engineering fundamentals.

- ML frameworks scikit-learn and PyTorch or TensorFlow.

- MLOps tooling MLflow, Kubeflow, DVC, or equivalent.

- Model serving FastAPI, BentoML, KServe, or Triton.

- Containers Docker, with working knowledge of Kubernetes.

- GenAI / LLM hands-on RAG, embeddings, vector databases (pgvector, Milvus, FAISS, or similar), and LLM / agent frameworks (LangChain, LlamaIndex, or equivalent).

- Cloud ML Azure ML preferred.

- Data comfortable with SQL; Spark / PySpark a plus.

Preferred / Nice to Have :

- Agentic AI / multi-agent orchestration and tool-use.

- Grounding LLMs to structured data / semantic layers (text-to-SQL, semantic retrieval).

- Fine-tuning, prompt optimisation, and LLM evaluation frameworks.

- Real-time / streaming ML and online inference.

- Experience deploying AI for enterprise or regulated customers.