Job Title: AI/ML Engineer
Experience: 5+ years
Work Mode: Remote
Key Responsibilities
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.
Required Skills & Qualifications
Experience
- 5+ years of experience in AI/ML, Data Science, AI Engineering, or related domains.
- Proven experience delivering production-grade AI/ML solutions.
Technical Skills
Programming & ML
- Strong proficiency in Python.
- Experience with:
- NumPy
- Pandas
- Polars
- Scikit-learn
- XGBoost
- TensorFlow
- PyTorch
Machine Learning
- Strong understanding of:
- Regression
- Classification
- Clustering
- Recommendation systems
- Forecasting models
- Anomaly detection
- Ranking systems
- Similarity search
- Optimization techniques
Generative AI
- Hands-on experience with:
- LLM applications
- RAG architecture
- Prompt engineering
- Embedding models
- Semantic search
- Hybrid search
- AI agents
- MCP integrations
- Tool calling workflows
AI Frameworks & Platforms
- Experience with:
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
- AutoGen
Vector Databases & Search
- Experience with:
- Pinecone
- Weaviate
- FAISS
- Milvus
- Elasticsearch
- Vertex AI Vector Search
Cloud & Infrastructure
- Strong experience with GCP services and cloud-native architectures.
- Knowledge of:
- Docker
- Kubernetes
- GKE
- Serverless architectures
- Event-driven systems
MLOps
- Experience implementing:
- Model monitoring
- Experiment tracking
- Feature stores
- Model registry
- Logging and observability
- CI/CD for ML systems
Preferred Qualifications
- Experience in retail, e-commerce, finance, analytics, customer service, or enterprise automation domains.
- Experience with:
- Vertex AI Search
- Google Commerce Search
- Dialogflow CX / Google CX Agent
- OR-Tools
- Exposure to enterprise AI governance, scalability, and responsible AI practices.
