Machine Learning Engineer

Skills:

Large Language Models, Conversational AI, Prompt Engineering, Python, Memory Systems

Join a visionary company in the software development industry, revolutionizing how people interact and communicate through AI-powered platforms. Our client is building state-of-the-art conversational intelligence systems that blend adaptive AI, large language models, and behavioral science to create emotionally intelligent user experiences. As a Machine Learning Engineer, you will architect and enhance the intelligence layer behind a next-generation AI platform, collaborating with industry experts in a fast-paced, innovative, and early-stage environment.

• Design and develop conversational AI systems powered by large language models (LLMs).
• Build and optimize AI response pipelines, prompt orchestration, and conversational workflows.
• Develop memory and context-management systems to ensure conversational continuity across sessions.
• Create governed response frameworks enforcing behavioral rules, safety constraints, and response consistency.
• Advance model behavior through prompt engineering, fine-tuning, evaluation, and reinforcement techniques.
• Architect adaptive and personalized AI interactions based on user context and evolving needs.
• Enable AI decision-making, contextual reasoning, and workflow selection beyond text generation.
• Evaluate and continually improve model quality, latency, reliability, and scalability.
• Collaborate with product and engineering leadership to experiment and develop new features.
• Stay ahead of emerging trends in LLMs, conversational AI, agentic systems, and AI safety/alignment.

• Proven experience building conversational AI, AI assistants, or agentic AI systems.
• Deep hands‑on expertise with large language models and modern LLM ecosystems.
• Strong command of prompt engineering, fine‑tuning, model evaluation, or RLHF workflows.
• Experience with conversational state management, memory systems, and maintaining context.
• Demonstrated ability to implement governed/constrained AI with safety or policy controls.
• Advanced Python development skills and proficiency with ML/AI frameworks.
• Skilled in using APIs, orchestration frameworks, vector databases, and retrieval systems.
• Track record of rapidly prototyping and iterating in dynamic, fast‑paced settings.
• Strong analytical, problem‑solving, and product intuition abilities.
• Comfortable navigating ambiguous early‑stage product environments.

• Experience creating adaptive or personalized AI systems.
• Familiarity with multi‑agent workflows or AI decision‑making architectures.
• Knowledge of frameworks such as Lang Chain, Lang Graph, Llama Index, Hugging Face, or PyTorch.
• Expertise in RAG pipelines, embeddings, vector search, and advanced memory architectures.
• Background in behavioral AI, communication technology, coaching, wellness, or healthcare‑focused platforms.
• Previous exposure to startup or early‑stage company environments.