Senior Artificial Intelligence and Machine Learning Engineer

About the Role 

We're looking for a Senior AI/ML Engineer who can design, build, and deploy scalable ML, GenAI, and Agentic AI systems across cloud environments (GCP preferred) with strong focus on productionization, automation, and business impact. You'll work across demand forecasting, RAG-based intelligent applications, autonomous multi-agent systems, and enterprise AI integration. 

Responsibilities 

• Build end-to-end ML/AI pipelines (data → model → deployment → monitoring) 

• Develop and deploy ML, Deep Learning, NLP, and GenAI models in production 

• Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering 

• Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory 

• Build and optimize time series forecasting models (demand forecasting, inventory planning) 

• Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance 

• Optimize models for performance, cost, and latency 

• Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications 

• Design scalable LLM inference architectures for efficient deployment 

• Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams 

• Debug, optimize, and enhance ML models for quality and performance improvements 

• Mentor team members and present technical findings to diverse audiences 

• Stay current with AI/GenAI trends and evaluate emerging tools and frameworks 

Mandatory Skills 

1. Programming & Core 

• Python — strong, production-grade coding 

• SQL — proficient 

• Data Structures & Algorithms 

• Git 

2. Machine Learning & Deep Learning 

• Regression, Classification, Clustering, Dimensionality Reduction 

• Ensemble Models (Random Forest, XGBoost, LightGBM) 

• CNN, RNN, LSTM, Transformers 

• Frameworks: Scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, PyTorch 

3. Statistics & Mathematics 

• Probability (Bayesian, Frequentist), Hypothesis Testing, A/B Testing 

• Regression (Linear, Logistic, GLM), Time Series Analysis 

• Optimization (convex/non-convex) 

• Libraries: NumPy, SciPy, Statsmodels 

4. ML Pipelines & MLOps 

• Building end-to-end ML pipelines in production (training, serving, monitoring) 

• MLOps tools: MLflow, Kubeflow, Vertex AI Pipelines 

• Model monitoring, drift detection, and governance 

5. Cloud — GCP (Primary) 

• Vertex AI (model training, pipelines, endpoints) 

• BigQuery, Cloud Storage, Dataproc (PySpark) 

• Cloud Composer (Airflow), Cloud Run 

6. Generative AI & LLMs 

• LLMs, advanced prompt engineering 

• RAG pipelines — retrieval, chunking, embeddings, vector search 

• VectorDBs: FAISS, Pinecone, Weaviate, ChromaDB, pgvector 

• Frameworks: LangChain, LlamaIndex, Hugging Face Transformers 

7. Agentic AI 

• Autonomous agents, multi-agent systems 

• Tool calling, planning, memory, workflow orchestration 

• Frameworks: LangGraph, CrewAI, AutoGen 

• Protocols: MCP (Model Context Protocol), A2A (Agent-to-Agent) 

8. Time Series / Demand Forecasting 

• Experience building forecasting models for business prediction 

• Time series techniques and retail/supply chain forecasting 

9. NLP 

• Text preprocessing, embeddings, NER, classification, sentiment analysis 

• Semantic search 

• Frameworks: Hugging Face Transformers, spaCy, NLTK 

10. Soft Skills 

• Strong communication — can present technical concepts to non-technical audiences 

• Mentoring ability — can guide and uplift junior team members 

• Analytical thinking with ability to translate business problems into AI solutions 

• Comfortable working in Agile, cross-functional teams 

Good to Have 

• LLM fine-tuning (LoRA, PEFT, or full fine-tune on Vertex AI) 

• LLM serving & inference optimization (vLLM, GPU memory optimization, model quantization) 

• Spark / PySpark for large-scale data processing 

• Computer Vision (image classification, object detection, OCR/Document AI, YOLO, Detectron2) 

• Recommendation Systems (collaborative filtering, content-based) 

• Microservices architecture and cloud-based deployments 

• FastAPI / Flask for API development 

• MongoDB for data handling and persistence 

• Multi-cloud exposure (AWS SageMaker, S3, Lambda, ECS/EKS, Step Functions) 

• Reinforcement Learning 

• Graph ML / Knowledge Graphs 

• Distributed computing (Spark, Ray) 

• ONNX / TensorRT for model optimization 

• Responsible AI / Explainability 

• Enterprise Agent Frameworks (Google ADK, AWS Bedrock Agents, Semantic Kernel) 

• Retail / E-commerce domain experience 

What Makes You Stand Out 

• Built autonomous agents that reason, use tools, and act independently in production 

• Deployed RAG systems at scale with real users 

• Experience with multi-agent orchestration (planner-executor patterns) 

• Built GPU-optimized LLM serving infrastructure 

• Worked on retail use cases — demand forecasting, recommendations, dynamic pricing, customer segmentation 

• Built microservices-based AI applications at enterprise scale 

• Measurable business impact from your AI deployments 

Success Criteria 

• Production-grade AI systems running reliably at scale 

• Scalable, automated ML/GenAI pipelines 

• Effective GenAI & Agentic AI deployments solving real business problems 

• Measurable business impact and stakeholder satisfaction