Senior Lead AI Engineer

Wilco Source; Pune District; Hybrid; INR 3,500,000 - 5,500,000; Full time

Job summary

Wilco Source is seeking a Senior Lead AI Engineer to architect and deliver scalable ML systems on GCP, focusing on personalization, forecasting and NLP. You will drive end-to-end pipelines, feature stores, and real-time inference while coordinating with product and data teams across a hybrid Pune setup.

Ideal candidates have hands-on experience with Vertex AI, data pipelines, vector search, and governance. Strong communication and mentoring skills are essential to lead multi-disciplinary teams

Qualifications

• Experience designing, building and deploying ML models at scale in cloud environments.
• Strong knowledge of GCP tools like Vertex AI, BigQuery and Dataflow.
• Hands-on with vector search, RAG, and agentic workflows; governance and safety patterns.

Responsibilities

• Design, build, and productionize ML models for personalization, forecasting, anomaly detection, and NLP on GCP (Vertex AI, BigQuery ML, Dataflow).
• Develop scalable data and feature pipelines; implement feature stores (Feast) and streaming ingestion (Pub/Sub, Kafka) for real-time inference.
• Implement agentic AI patterns: multi-agent orchestration, tool-use, retrieval-augmented generation (RAG), and function calling with guardrails (policy/PII filters).
• Optimize online inference for latency and cost using Vertex AI Endpoints, GPU/TPU here applicable, and autoscaling on GKE.
• Establish experiment frameworks (A/B, interleaving, bandits) and offline evaluation precision/recall, ROC-AUC, NDCG, MAP).
• Integrate vector search (Vertex Matching Engine / FAISS / Elasticsearch) for semantic retrieval and recommendations.
• Ensure privacy, security, and compliance (GDPR/CCPA); apply differential privacy where needed and follow model governance practices.
• Document designs, review code, and collaborate with product, data engineering, and platform teams.

Skills

Python

ML system design

MLOps

Distributed computing

GCP

RAG

Vector search

Communication

Tools

TensorFlow

PyTorch

scikit-learn

Vertex AI

BigQuery

Dataflow

Kubernetes

Docker

Job description

Position : Senior Lead AI Engineer

Job Type: Full-time

Location: Pune (2-days from Office)

Work timings: 3pm to 12am IST(Due to US project, based on project need)

Notice Period: Immediate to 30 days

Responsibilities

• Design, build, and productionize ML models for personalization, forecasting, anomaly detection, and NLP on GCP (Vertex AI, BigQuery ML, Dataflow).
• Develop scalable data and feature pipelines; implement feature stores (Feast) and streaming ingestion (Pub/Sub, Kafka) for real-time inference.
• Implement agentic AI patterns: multi-agent orchestration, tool-use, retrieval-augmented generation (RAG), and function calling with guardrails (policy/PII filters).
• Optimize online inference for latency and cost using Vertex AI Endpoints, GPU/TPU here applicable, and autoscaling on GKE.
• Establish experiment frameworks (A/B, interleaving, bandits) and offline evaluation precision/recall, ROC-AUC, NDCG, MAP).
• Integrate vector search (Vertex Matching Engine / FAISS / Elasticsearch) for semantic retrieval and recommendations.
• Ensure privacy, security, and compliance (GDPR/CCPA); apply differential privacy where needed and follow model governance practices.
• Document designs, review code, and collaborate with product, data engineering, and platform teams.

Desired Technical Skills

Languages

• Python (primary)
• Java/Scala (nice to have)

Frameworks

• TensorFlow
• PyTorch
• scikit-learn
• JAX (optional)

Pipelines & Features

• Apache Beam/Dataflow
• Airflow/Cloud Composer
• Feast

RAG & Agents

• LangChain/LlamaIndex
• Function calling
• Toolformer patterns
• Vector databases (FAISS, Chroma, Elasticsearch)

Data & Streaming

• BigQuery
• Spark
• Kafka
• Pub/Sub

Serving

• Vertex AI Endpoints
• KFServing
• Triton
• Docker
• Kubernetes (GKE)

Observability

• MLflow
• Vertex Experiments/Model Registry
• Prometheus
• Grafana

Cloud

• GCP (Vertex AI, BigQuery, GCS) - Primary
• Familiarity with AWS (SageMaker) and Azure ML

Preferred Experience & Capabilities

• Delivered ML systems at scale (batch + real-time) with measurable business impact.
• Hands‑on experience with vector search, RAG, and agentic workflows using tools/actions and governance (safety filters, jailbreak protection).
• Expertise in feature engineering, data quality, and drift detection (data/model).
• Strong understanding of IR/ranking metrics and online experimentation.
• Ability to mentor, perform code reviews, and contribute to architectural decisions.
• Excellent communication with cross‑functional stakeholders.

Note:Primary development environment is GCP (Google Cloud Platform). Flexibility to work with AWS and Azure is desired for portability and interoperability.