Ai Ml Engineer

We are seeking a highly skilled and innovative AI/Generative AI Engineer to join our dynamic team. This pivotal role involves designing, developing, and deploying cutting-edge Large Language Model (LLM)-based solutions, driving significant impact through advanced AI capabilities and strategic prompt engineering.

Detailed Roles:

This role will primarily focus on the end-to-end lifecycle of Generative AI applications, from conceptualization to deployment. You will be responsible for conducting rigorous evaluation of LLMs, ensuring optimal performance and reliability for diverse applications. A key aspect of your work will involve leveraging vector embeddings & similarity search to build robust information retrieval and contextual understanding systems. You will also pioneer advanced prompt engineering techniques to unlock the full potential of LLMs, guiding their responses and behaviors effectively.

You will be instrumental in the development and deployment of scalable ML algorithms and sophisticated agents / multi-agent systems, contributing to a new generation of intelligent applications. This includes implementing best-in-class MLOps practices to streamline development, continuous integration, and automated deployment of AI models, ensuring operational excellence and stability. Your expertise will be crucial in performing detailed model selection & trade-off analysis, identifying the most suitable AI solutions for various business challenges while balancing performance, cost, and ethical considerations.

Collaboration will be a cornerstone of this position, working closely with cross-functional teams including data scientists, software engineers, and product managers. You will translate complex business requirements into technical specifications, driving innovation and delivering impactful AI solutions. This role offers significant growth opportunities to lead projects, mentor junior engineers, and contribute to the strategic direction of our Generative AI initiatives, staying at the forefront of AI advancements.

As a senior contributor, you will regularly present your findings and project outcomes to stakeholders, influencing technical decisions and fostering a data-driven culture. You will be expected to continuously explore emerging AI technologies, contributing to our knowledge base and identifying opportunities for competitive advantage.

Required Qualifications:

A Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field is highly preferred, or equivalent practical experience. Candidates must possess a minimum of 5+ years of experience in AI/ML development, with at least 3 years focused specifically on Generative AI and Large Language Models. Expert proficiency in Python and its associated ML/AI libraries (e.g., TensorFlow, PyTorch, Hugging Face) is mandatory. Strong demonstrable experience in prompt engineering, LLM evaluation, and vector embeddings is essential.

Preferred Qualifications:

Experience with cloud platforms (e.g., AWS, Azure, GCP) for MLOps and AI deployment is a significant plus. Familiarity with distributed computing frameworks and advanced AI agent architectures is highly desirable. Certifications in relevant AI or cloud technologies will be advantageous.

Roles & Responsibilities

• Design and implement Generative AI and Large Language Model (LLM)-based solutions.
• Conduct thorough evaluation, benchmarking, and fine-tuning of LLMs for optimal performance.
• Develop and optimize vector embeddings and similarity search algorithms for enhanced information retrieval.
• Apply advanced prompt engineering techniques to maximize LLM effectiveness and control model behavior.
• Implement MLOps practices for reliable deployment, monitoring, and continuous integration of AI models.
• Research, select, and analyze trade-offs of appropriate ML algorithms and models for diverse applications.
• Develop, integrate, and manage AI agents and multi-agent systems to solve complex problems.

Competencies For The Job

• Expertise in Artificial Intelligence and Generative AI principles and applications.
• Profound understanding of Large Language Models (LLMs) and their underlying architectures.
• Ability to critically evaluate, benchmark, and improve the performance of LLMs.
• Strong analytical skills for comprehensive model selection and trade-off analysis.
• Proficiency in designing, developing, and implementing scalable machine learning systems.
• Adeptness in developing and optimizing prompt engineering strategies for various LLM use cases.
• Capability to design, integrate, and manage autonomous and multi-agent AI systems.