Generative AI Engineer

About Us:

Solytics Partners is a Global Analytics firm, recognized with multiple industry awards for innovation and excellence. Our team comprises experts with deep domain knowledge in risk, analytics, AI/ML, AML/FCC, and fraud. By converging this expertise with cutting-edge technologies like AI, Machine Learning, Generative AI, and Large Language Models (LLMs), we deliver powerful automated platforms and incisive point solutions.

Our offerings enable clients to streamline and future-proof their risk, AML, and analytics processes, comply seamlessly with global regulations, and safeguard financial systems. Whether it's solving complex challenges or driving operational efficiency, Solytics Partners is committed to empowering organizations with transformative tools to stay ahead in an evolving regulatory landscape.

Job Overview:

We are looking for a highly skilled Python & Generative AI Engineer who can independently design, develop, deploy, and productionize AI-powered solutions from scratch.

The ideal candidate will have strong hands-on experience with Python, Generative AI, LLMs, RAG, AI Agents, Prompt Engineering, and Vector Databases, along with a strong software engineering mindset.

This role is ideal for an engineer who enjoys solving complex problems, taking complete ownership of AI products, and converting business/product requirements into scalable, reliable, and production-grade AI solutions.

Key Responsibilities:

• Design and develop end-to-end Generative AI and LLM-powered applications using Python.
• Independently take AI solutions from requirements and architecture through development, deployment, and production support.
• Build and optimize RAG-based applications, including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
• Design and implement AI Agents using LLMs, tool/function calling, orchestration, and appropriate memory/state mechanisms.
• Develop effective prompt engineering strategies to improve accuracy, reliability, and consistency of LLM outputs.
• Work with Vector Databases for high-performance semantic search and retrieval.
• Build data pipelines to ingest, clean, transform, and process large volumes of structured and unstructured data.
• Design scalable APIs and backend services for AI applications using frameworks such as FastAPI or Flask.
• Integrate LLMs, AI services, databases, APIs, and other external systems into production applications.
• Deploy and operate AI applications using Azure/AWS, Docker, CI/CD, and MLOps practices.
• Implement monitoring, logging, evaluation, error handling, and performance optimization for production AI systems.
• Optimize AI applications for scalability, latency, reliability, and cost efficiency.
• Apply relevant Machine Learning and Data Science concepts to solve real-world product problems.
• Collaborate with product and engineering teams to translate requirements into practical AI solutions.
• Proactively identify technical challenges and independently develop effective solutions.
• Take ownership of the quality, reliability, and continuous improvement of AI applications in production.

Must-Have Skills:

• Strong hands-on Python programming experience.
• Strong practical experience with Generative AI and LLM-based applications.
• Hands-on experience building RAG systems.
• Hands-on experience with AI Agents/Agentic AI.
• Strong understanding and practical experience with Prompt Engineering.
• Hands-on experience with Vector Databases and semantic search.
• Experience building and deploying end-to-end AI applications to production.
• Strong experience processing and handling large volumes of structured and unstructured data.
• Good understanding of Machine Learning and Data Science fundamentals.
• Strong software engineering, debugging, problem-solving, and system-design skills.
• Ability to independently own and deliver production-grade AI solutions.

Preferred Skills:

• Experience with AWS or Azure cloud platforms.
• Experience with FastAPI or Flask.
• Hands-on experience with Docker and containerized deployments.
• Understanding of CI/CD and MLOps practices.