Job Title

We’re Hiring: AI Engineer – Agentic AI, NLP, Autonomous Systems We are looking for an experiencedAI Engineerto join an exciting and fast-growing team, focused on building next-generation intelligent systems in the legal tech space. As an AI Engineer, you will design, develop, and deploy autonomous, goal-driven agents that reason, learn, and act in dynamic environments. Your work will power innovative features in advanced contract lifecycle management platforms by leveraging the latest in large language models, reinforcement learning, and autonomous AI frameworks. What You’ll Do: Architect and implement scalable agentic AI systems that execute complex workflows and reason over legal data. Research, prototype, and productionize ML/DL models, particularly in NLP/NLU. Build intelligent legal agents capable of document interpretation, decision-making, and collaborating with users or other agents to complete multi-step tasks. Utilize frameworks like LangChain, AutoGen, OpenAI Function Calling, and Semantic Kernel to build multi-agent workflows. Fine-tune and integrate large language models using PEFT, LoRA, and RAG techniques for domain-specific challenges. Design robust infrastructure for managing AI lifecycle: training, inference, monitoring, and continuous learning. Collaborate closely with legal experts, product managers, and engineers to build explainable and trustworthy AI systems. Contribute to the development of AI strategies for agent-based automation in contract management. What You’ll Need: Strong background in Computer Science, Software Engineering, or Data Science, with deep expertise in ML and NLP. Proven experience building or integrating agentic AI systems (AutoGPT-style agents, goal-oriented LLM pipelines, multi-agent frameworks). Proficiency in Python, HuggingFace Transformers, LangChain, PyTorch, TensorFlow, and Spacy. Experience developing and scaling ML models (LSTMs, BERT, Transformers) in production. Understanding of LLM training (OpenAI, LLAMA, Falcon), embeddings, and prompt engineering. Hands-on experience with Reinforcement Learning (PPO, RLHF, RLAIF). Experience with text and semantic information extraction from structured/unstructured documents (PDFs, images). Comfortable in Agile/Scrum environments and cross-functional collaboration. Passion for building autonomous systems that solve real-world problems.