AIML Architect

We are seeking a highly experienced ML/AI Architect to design, build, and scale enterprise-grade AI/ML and GenAI solutions within our Digital Transformation Practice. The role requires strong technical leadership across ML system architecture, LLM-based solution design, MLOps automation, and cloud-native data engineering. The ideal candidate will drive innovation, establish best practices, and deliver production-ready AI solutions at scale

Key Skills & Competencies

• LLMs / GenAI: GPT-4/5, Llama 3/3.5, Qwen, Claude, RAG, prompt engineering, LoRA/QLoRA, embeddings
• ML / DL: PyTorch, TensorFlow, Transformers, CNNs, XGBoost, NLP/NLU, NER
• MLOps: MLflow, Airflow, Kubeflow, SageMaker, Vertex AI, Azure ML, CI/CD/CT, monitoring
• Data Engineering: PySpark, SQL, ETL/ELT pipelines, orchestration, data quality
• Cloud & DevOps: AWS, Azure, GCP, Kubernetes, Docker, Terraform, GPU stack
• Other: Vector DBs (Pinecone, Weaviate, Chroma, Milvus), observability, model optimization, model explainability (SHAP/LIME)

Roles and Responsibilities

• Architect end-to-end ML systems covering data ingestion, feature engineering, model training, deployment, and monitoring.
• Design scalable ML pipelines for classical ML, deep learning, and GenAI workloads including LLMs, RAG, embeddings, and semantic search.
• Select optimal algorithms, frameworks, and cloud platforms to address complex business and technical challenges.
• Implement model optimization strategies such as quantization, pruning, distillation, and LoRA/QLoRA fine-tuning.
• Build LLM-powered solutions using prompt engineering, knowledge grounding, agents, and retrieval-augmented generation.
• Work with top LLMs (GPT-4/5, Llama 3/3.5, Qwen, Claude, Gemini) and integrate vector DBs like Pinecone, Weaviate, Chroma, Milvus.
• Evaluate and improve LLM and ML model performance, reduce hallucinations, and ensure security and compliance.
• Lead development of ML, NLP, NER, and deep learning models using Transformers, CNNs, XGBoost, and other advanced architectures.
• Drive data science workflows including EDA, feature engineering, statistical modeling, and explainability (SHAP/LIME).
• Architect MLOps pipelines (CI/CD/CT), enabling model versioning, monitoring, drift detection, and automated retraining.
• Deploy models at scale using Kubernetes, Docker, Terraform, serverless components, and GPU-based environments with strong observability.
• Collaborate with cross-functional teams, lead technical discussions and POCs, and mentor ML/AI teams to deliver enterprise-grade solutions.