AI/ML Engineer

Role overview

• Build, fine-tune, evaluate, and optimize Large Language Models (LLMs) for client-specific use cases such as document intelligence, chatbot automation, code generation, and workflow orchestration.
• Develop RAG (Retrieval-Augmented Generation) pipelines using enterprise knowledge bases.
• Implement prompt engineering, guardrails, hallucination reduction strategies, and safety frameworks.
• Work with transformer-based architectures (GPT, LLaMA, Mistral, Falcon, etc.) and develop optimized model variants for low-latency and cost-efficient inference.
• Develop scalable ML systems including feature pipelines, training jobs, and batch/real-time inference services.
• Build and automate training, validation, and monitoring workflows for predictive and GenAI models.
• Perform offline evaluation, A/B testing, performance benchmarking, and business KPI tracking.
• Build and maintain end-to-end MLOps pipelines using:
• AWS SageMaker, Databricks, MLflow, Kubernetes, Docker, Terraform, Airflow
• Manage CICD pipelines for model deployment, versioning, reproducibility, and governance.
• Implement enterprise-grade model monitoring (data drift, performance, cost, safety).
• Maintain infrastructure for vector stores, embeddings pipelines, feature stores, and inference endpoints.
• Build data pipelines for structured and unstructured data using:
• Snowflake, S3, Kafka, Delta Lake, Spark (PySpark)
• Work on data ingestion, transformation, quality checks, cataloging, and secure storage.
• Ensure all systems adhere to Apexon and client-specific security, IAM, and compliance standards.
• Partner with product managers, data engineers, cloud architects, and QA teams.
• Translate business requirements into scalable AI/ML solutions.
• Ensure model explainability, governance documentation, and compliance adherence.