AI/ML Engineer – Generative AI Specialist

Job Title: AI/ML Engineer – Generative AI Specialist

Experience Range: 5+ years

Must-Have Skills:

• AI/ML frameworks (PyTorch, TensorFlow) and Python programming.
• Experience with AWS services, including AWS Bedrock and Lambda functions.
• Hands-on expertise in LangChain (agents, chains, memories, parsers, document loaders).
• Proficiency in developing and optimizing GenAI, NLP models, and CrewAI.
• Building and deploying RAG (Retrieval-Augmented Generation) systems, chatbots, and AI applications using OpenAI APIs, Ollama, Llama, and llamaparse.
• Advanced prompt engineering techniques, including Chain of Thought (CoT) prompting.
• Experience with Docker, CI/CD pipelines, writing YML files, and managing repositories in GitHub.
• Strong understanding of system design principles, including security, scalability, and performance optimization.

Good-to-Have Skills:

• Experience in responsible AI practices, including guardrails and moderation mechanisms.
• Knowledge of event-driven architecture and AWS services beyond Bedrock and Lambda.
• Familiarity with cloud deployments and Kubernetes.
• Experience with large-scale AI model training and fine-tuning.

Key Responsibilities:

• Design and implement AI/ML solutions using cutting-edge technologies, ensuring scalability, efficiency, and alignment with business objectives.
• Develop and optimize LangChain components, CrewAI, GenAI, and NLP models tailored to specific applications.
• Build and deploy production-ready RAG systems, chatbots, and AI-driven applications leveraging OpenAI APIs, Ollama, Llama, and llamaparse.
• Utilize AWS Bedrock and other AWS services to deliver high-performance AI solutions.
• Apply prompt engineering techniques, including Chain of Thought (CoT) prompting, to enhance AI interactions.
• Develop and maintain CI/CD pipelines, manage YML files, and use GitHub for version control.
• Implement containerization using Docker and deploy AI solutions in cloud environments.
• Ensure solutions adhere to best practices in system design, addressing security, performance, and efficiency considerations.
• Incorporate responsible AI practices and ensure compliance with ethical AI guidelines.
• Collaborate with cross-functional teams to translate AI insights into impactful solutions.
• Communicate complex technical concepts effectively to both technical and non-technical stakeholders.