Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or related fields
4+ years of experience in building AI applications with NLP and deep learning
2 years of experience in building production grade Gen AI applications
Experience with cloud platforms such as AWS, Azure, or Google Cloud
Advanced Programming skills in Python
Solid understanding of Gen AI, LLMs, fine tuning, RAG, Agentic AI, prompt engineering
Experience with LLM based application development using frameworks like LangChain, LangGraph, LangSmith, Google GenAI SDK
Understanding of data privacy, AI Ethics, Responsible AI, and model governance
Requirements:
Collaborate with stakeholders to identify AI business problems
Develop innovative AI solutions using Gen AI and frameworks
Fine-tune and deploy generative AI models
Integrate generative models into scalable applications
Stay updated with advancements in Gen and Agentic AI
Deploy AI agents into production environments
Implement Agent Chaining solutions
Develop and refine prompts for language models
Maintain clear and comprehensive technical documentation
Mentor junior Python engineers
Advocate for AI adoption within the team
Job description
At 3Pillar, we leverage cutting-edge technologies to drive innovation and deliver impactful solutions for our clients. As a Senior AI Engineer, you will lead the design and implementation of scalable AI systems and provide technical mentorship to junior team members. You’ll also evaluate model architectures, explore cutting-edge techniques, and ensure solutions are both impactful and ethical.
Responsibilities:
Collaborate with stakeholders, including product managers, architects, and software engineers to identify business problems that can be solved using AI and develop innovative AI solutions using Gen AI, Agentic AI and similar technologies and frameworks.
Understanding the business requirement to implement, integrate and deploy Generative AI product & features into production environments.
Fine-tune and deploy generative AI models for tasks like text, image, video or audio generation.
Integrate generative models into scalable applications and ensure optimal production grade performance.
Stay updated with advancements in Gen and Agentic AI and implement best practices.
Deploy AI agents into production environments, ensuring performance, scalability, security, and reliability.
Agent Chaining: Implement solutions that enable multiple AI agents to collaborate and solve complex tasks.
Develop and refine prompts to maximize performance and accuracy of language models.
Maintain clear and comprehensive technical documentation.
Mentor and provide technical guidance to junior Python engineers and team members.
Advocating for AI adoption inside the team and inspiring, supporting others.
Qualification:
Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or related fields.
A minimum of 4+ years of experience in building AI applications with NLP and deep learning, with around 2 years of experience in building production grade Gen AI applications.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Advance Programming skills in Python
Solid understanding of Gen AI, LLMs, LLM fine tuning, RAG, MCP, Agentic AI, and prompt engineering.
Ability to work with different LLM models and choose one over another based on a use case
Experience with LLM based application development using frameworks such as LangChain, LangGraph, LangSmith, and Google GenAI SDK.
Understanding of data privacy, AI Ethics, Responsible AI, and model governance
Strong analytical and problem-solving skills, with attention to detail.
Excellent communication skills, with the ability to present complex technical concepts to both technical and non-technical audiences.
Proven ability to work independently and with a team.
Data Engineering (Good to have)
Additional Experience Desired:
Good understanding of Software Engineering processes.
Experience in working with huge datasets in a production environment.
Experience with vector databases (FAISS, Pinecone), RAG, Hugging Face.
Contributions to research papers, blogs, or open-source projects.
