Senior AI Engineer
As a Senior AI Engineer, you'll be at the forefront of applying cutting-edge AI technologies to solve real-world challenges. You'll work with a team dedicated to building AI-powered solutions that help global enterprises understand, manage, and automate complex processes across multiple markets and jurisdictions.
Core Responsibilities:
Product Development & Innovation:
Identify and implement opportunities where AI can create tangible value for customers.
Design and develop production-grade AI solutions that automate complex tasks.
Build and optimize advanced NLP models for document analysis and interpretation.
Contribute to finding the right ML framing of industry challenges.
Create scalable AI systems that can handle diverse requirements across different markets.Technical Leadership:
Take algorithms from proof-of-concept to production, ensuring robustness and scalability.
Establish processes for large-scale data analysis, model development, validation, and deployment.
Build and maintain ML infrastructure that can evolve with growing needs.
Implement monitoring systems to track model performance and accuracy.
Ensure AI solutions maintain high standards of explainability and ethical considerations.Cross-functional Collaboration:
Work closely with product managers and subject matter experts to translate business requirements into technical solutions.
Partner with engineering teams to integrate AI capabilities into the core platform.
Contribute to technical documentation and knowledge sharing.
Participate in architecture discussions and technical decision-making.Key Skills:
5+ years of hands-on experience in production environments, with a strong track record of designing and architecting distributed systems.
Experience developing and deploying production-grade software using ML frameworks such as TensorFlow, PyTorch, etc.
Deep understanding of engineering and infrastructure best practices, with a focus on the ML software development lifecycle.
Expertise in building, fine-tuning, and optimizing Large Language Models (LLMs), transformer architectures, and advanced NLP systems using frameworks like Hugging Face and OpenAI SDKs.
Strong background in modern deep learning techniques, LLMs, and data-intensive applications.
Familiarity with state-of-the-art agentic AI frameworks (e.g., LangChain, AutoGen, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, Google's ADK) and experience orchestrating multi-agent workflows.
Knowledge of techniques for optimizing ML inference performance.
Hands-on expertise designing and managing batch and real-time ML pipelines/workflows for structured and unstructured data on AWS (SageMaker, ECS, EKS, Airflow).
Familiarity with LLMOps and observability tools (Langsmith, Opik, Ragas, Weights & Biases) for monitoring, evaluation, and governance.
Excellent communication skills and the ability to collaborate effectively across teams