Senior AI/ML Engineer
Budget: $20 – $23 USD
Senior AI/ML Engineer
Key Responsibilities:
• Design and implement AI/ML solutions using advanced frameworks and technologies, ensuring scalability, efficiency, and alignment with business goals.
• Develop and optimize LangChain (agents, chains, memories, parsers, document loaders), CrewAI, GenAI, and NLP models tailored to specific use cases.
• Build and deploy production-ready RAG (Retrieval-Augmented Generation) systems, chatbots, and other AI-driven applications using OpenAI APIs, Ollama, Llama, and llamaparse.
• Leverage Azure and GCP services, including Lambda functions (event-driven architecture in Python), to deliver high-performing AI solutions.
• Apply advanced prompt engineering techniques, including Chain of Thought (CoT) prompting, for enhancing AI model interactions.
• Develop and maintain CI/CD pipelines, write and manage YML files, and work with GitHub for version control.
• Use Docker effectively, including executing Docker commands for containerization and deployments in cloud environments.
• Ensure solutions adhere to best practices in system design, addressing trade-offs, security, performance, and efficiency.
• Implement responsible AI practices, incorporating guardrails and moderation mechanisms.
• Collaborate with cross-functional teams to translate AI insights into impactful solutions.
• Communicate complex technical concepts effectively to both technical and non-technical stakeholders.
Qualifications:
• 4+ years of hands-on experience with AI/ML frameworks (e.g., PyTorch, TensorFlow) and programming in Python.
• Expertise in AWS services, particularly AWS Bedrock and Lambda functions.
• Demonstrated experience with RAG systems, chatbot development, and working with GenAI technologies like LLMs and OpenAI APIs.
• Deep understanding of LangChain components and advanced NLP techniques.
• Strong knowledge of Docker and CI/CD pipelines, with experience writing YML files and managing repositories in GitHub.
• Familiarity with best practices in system design, including security, performance optimization, and scalability.
• Proven ability to write high-quality production code and work in collaborative environments.
Key Responsibilities:
• Design and implement AI/ML solutions using advanced frameworks and technologies, ensuring scalability, efficiency, and alignment with business goals.
• Develop and optimize LangChain (agents, chains, memories, parsers, document loaders), CrewAI, GenAI, and NLP models tailored to specific use cases.
• Build and deploy production-ready RAG (Retrieval-Augmented Generation) systems, chatbots, and other AI-driven applications using OpenAI APIs, Ollama, Llama, and llamaparse.
• Leverage Azure and GCP services, including Lambda functions (event-driven architecture in Python), to deliver high-performing AI solutions.
• Apply advanced prompt engineering techniques, including Chain of Thought (CoT) prompting, for enhancing AI model interactions.
• Develop and maintain CI/CD pipelines, write and manage YML files, and work with GitHub for version control.
• Use Docker effectively, including executing Docker commands for containerization and deployments in cloud environments.
• Ensure solutions adhere to best practices in system design, addressing trade-offs, security, performance, and efficiency.
• Implement responsible AI practices, incorporating guardrails and moderation mechanisms.
• Collaborate with cross-functional teams to translate AI insights into impactful solutions.
• Communicate complex technical concepts effectively to both technical and non-technical stakeholders.
Qualifications:
• 4+ years of hands-on experience with AI/ML frameworks (e.g., PyTorch, TensorFlow) and programming in Python.
• Expertise in AWS services, particularly AWS Bedrock and Lambda functions.
• Demonstrated experience with RAG systems, chatbot development, and working with GenAI technologies like LLMs and OpenAI APIs.
• Deep understanding of LangChain components and advanced NLP techniques.
• Strong knowledge of Docker and CI/CD pipelines, with experience writing YML files and managing repositories in GitHub.
• Familiarity with best practices in system design, including security, performance optimization, and scalability.
• Proven ability to write high-quality production code and work in collaborative environments.