Generative AI Specialist (S04/S05)

About US:

Systango Technologies Limited (NSE: SYSTANGO) is a digital engineering company that offers enterprise-class IT and product engineering services to different size organizations. At Systango, we have a culture of efficiency - we use the best-in-breed technologies to commit quality at speed and world-class support to address critical business challenges. We leverage Gen AI, AI/Machine Learning and Blockchain to unlock the next stage of digitalization for traditional businesses. Our handpicked team is adept at web enterprise development, mobile apps, QA and DevOps. Sila, Cuentas, Youtility, Porsche, MGM Grand, Deloitte, Grindr, and Tawk.to are some of the top clients that have entrusted us to enhance their digital capabilities and build disruptive innovations. We believe in making the impossible, Possible and we do it literally.

Job Summary:

We are looking for an experienced Generative AI Specialist to lead the design and deployment of AI-driven applications using large language models (LLMs) and retrieval-augmented generation (RAG) techniques. In this role, you ll work with open-source models and cutting-edge tools, including LangChain, Hugging Face, and vector databases, to deliver scalable, high-impact generative AI solutions. You will also be responsible for advanced prompt engineering, customizing models to fit unique business requirements, and collaborating across teams to deliver AI solutions with technical precision and business value.

Key Responsibilities:

• Generative AI and NLP Development: Develop and deploy LLM-based applications, such as chatbots and virtual assistants, to enhance engagement and provide personalized experiences.
• Prompt Engineering: Design and optimize prompts to improve LLM responses, iteratively refining model outputs to ensure relevance, coherence, and adherence to business objectives.
• Model Customization: Fine-tune and customize open-source LLMs (e.g., LLaMA, Falcon, Mistral) on platforms like Hugging Face or local infrastructure to tailor models for specific business needs and use cases.
• RAG System Design: Build retrieval-augmented generation (RAG) systems using vector databases such as Weaviate, Pinecone, or FAISS to enable efficient, context-aware responses in generative AI applications.
• Backend and API Integration: Implement Python-based backend solutions, and integrate APIs to deploy generative AI models effectively within applications.
• Tool and Framework Utilization: Use tools like LangChain, Haystack, and Transformers for model orchestration, NLP processing, and workflow automation.
• Documentation and Client Engagement: Prepare Statements of Work (SOW) and Requests for Proposals (RFP), detailing project requirements, generative AI capabilities, and solution specifications for stakeholders.
• Cross-Functional Collaboration: Partner with data scientists, ML engineers, and business teams to gather requirements, define goals, and drive generative AI projects.

Required Skills and Qualifications:

• Generative AI Expertise: Strong background in NLP, generative AI, and LLMs, with hands-on experience working with open-source models like LLaMA, Falcon, and Mistral.
• Prompt Engineering Skills: Proficiency in designing and optimizing prompts to refine LLM outputs for specific applications.
• Python Proficiency: Advanced Python skills for backend development, API integration, and model customization.
• RAG and Vector Databases: Proven experience in developing RAG systems and working with vector databases (e.g., Weaviate, Pinecone, FAISS).
• Model Customization: Practical experience in fine-tuning and deploying open-source LLMs to adapt them for specialized use cases.
• Documentation Skills: Strong technical writing ability to produce SOWs, RFPs, and other documentation outlining project requirements and solution architectures.

Preferred Qualifications:

• AI and Cloud Certifications: Certifications in AI, machine learning, or cloud-based AI infrastructure (AWS, Azure, GCP).
• Advanced Model Development: Knowledge of model fine-tuning techniques, reinforcement learning with human feedback (RLHF), and transformer optimization.
• MLOps Experience: Familiarity with tools like MLflow, Weights Biases, or Neptune.ai for managing model experimentation, deployment, and monitoring.