Role Overview

We are seeking an experienced Senior AI Engineer / Architect to lead the design, development, and deployment of cutting-edge machine learning (ML), deep learning (DL), and generative AI solutions. This role involves full ownership of AI initiatives—from data strategies and model prototyping to production rollout and lifecycle management. You will collaborate with cross-functional teams to ensure technical solutions align with strategic business objectives.

Key Responsibilities

AI Architecture & Development

• Architect and implement scalable ML/DL pipelines: data ingestion, feature engineering, model training, evaluation, and serving.
• Design, prototype and productionize generative AI applications, including prompt engineering, LLM fine-tuning, and multimodal systems (vision + language).

MLOps & Infrastructure

• Define and enforce MLOps best practices: CI/CD for models, automated testing, monitoring, alerting, and continuous retraining.

Integration & Governance

• Integrate AI solutions with enterprise systems via robust APIs, ensuring compliance with data governance, security, and compliance.

Innovation & Research

• Explore and apply emerging AI techniques (GANs, VAEs, RAG, foundation models) to deliver innovative production features.

Leadership & Collaboration

• Mentor data scientists and engineers, promoting best practices and collaborative knowledge sharing.
• Partner with product managers and stakeholders to define AI roadmaps, success metrics, and present technical strategies.

Project Management

• Manage project lifecycles—from requirement gathering and design to deployment, performance tuning, and optimization—ensuring on-time, high-quality delivery.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
• 8+ years of experience, with at least 3 years in a technical leadership or architect role, AI/ML engineering, driving complex AI transformations across domains.
• Proven experience in working with generative AI technologies (LLMs, vision models, prompt engineering, GANs/VAEs), and RAG architectures, with hands on exposure to GenAI platforms (OpenAI, Azure OpenAI, Hugging Face, Amazon Bedrock)
• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Expertise in containerization (Docker), orchestration (Kubernetes), and pipeline tools (Airflow, MLflow).
• Experience with LangChain, LangGraph, AST-RAG, and agentic workflows.
• Familiarity with NLP, computer vision, OCR, and multimodal AI systems.
• Experience with cloud AI platforms: SageMaker, Azure ML, or Vertex AI.
• Exposure to big data tools: Spark, Hadoop.
• Experience in monitoring and logging dashboards like Prometheus, Grafana and ELK stack