AI/ML Engineer

AI/ML Engineer 5+ years

• AI/ML Solution Architecture
• Design and implement scalable AI/ML architectures for enterprise-grade applications.
• Lead end-to-end AI solution development including data pipelines, model development, deployment, monitoring, and optimization.
• Build reusable AI platforms, frameworks, and accelerators for organization-wide adoption.
• Generative AI & Agentic AI
• Architect and develop Generative AI applications using Large Language Models (LLMs).
• Design Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search.
• Build AI agent workflows with tool calling, multi-agent orchestration, and MCP-based integrations.
• Develop conversational assistants, coding assistants, automation agents, and enterprise copilots.
• Machine Learning Engineering
• Develop and optimize ML models for:
• Recommendation systems
• Demand forecasting
• Price prediction
• Customer segmentation
• Fraud detection
• Predictive analytics
• Search ranking and relevance
• Behavioral analytics
• Optimization systems
• Cloud & Platform Engineering
• Lead AI/ML implementations on cloud platforms, preferably Google Cloud Platform (GCP).
• Work with services such as:
• Vertex AI
• BigQuery
• Cloud Run
• GKE
• Pub/Sub
• Dataflow
• Vertex AI Search
• Cloud Storage
• Vector Search
• MLOps & Deployment
• Establish MLOps best practices including:
• Model versioning
• Experiment tracking
• Feature engineering pipelines
• Drift detection
• Monitoring and observability
• Automated retraining workflows
• Deploy scalable AI services using:
• FastAPI / Flask
• Docker
• Kubernetes
• REST APIs
• CI/CD pipelines
• Microservices
• Leadership & Collaboration
• Lead and mentor AI/ML engineers, data scientists, and backend engineering teams.
• Collaborate with product, business, and leadership stakeholders to translate business problems into AI-driven solutions.
• Evaluate emerging AI technologies and recommend strategic adoption opportunities.

• 5+ years of experience in AI/ML, Data Science, AI Engineering, or related domains.
• Proven experience delivering production-grade AI/ML solutions.

• Strong proficiency in Python.
• Experience with:
• NumPy
• Pandas
• Polars
• Scikit-learn
• XGBoost
• TensorFlow
• PyTorch

• Strong understanding of:
• Regression
• Classification
• Clustering
• Recommendation systems
• Forecasting models
• Anomaly detection
• Ranking systems
• Similarity search
• Optimization techniques

• Hands-on experience with:
• LLM applications
• RAG architecture
• Prompt engineering
• Embedding models
• Semantic search
• Hybrid search
• AI agents
• MCP integrations
• Tool calling workflows

• Experience with:
• LangChain
• LangGraph
• LlamaIndex
• CrewAI
• AutoGen

• Experience with:
• Pinecone
• Weaviate

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.