AI / GenAI / ML Engineer

About the opportunity

Role- AI / GenAI / ML Engineer Experience- 3+ Years Location: Flexible Role Overview: We are looking for an AI / GenAI / ML Engineer to design and build production-grade AI solutions using Generative AI, LLMs, Machine Learning, RAG, and AI Agents. The ideal candidate should combine strong software engineering skills with practical experience in developing, evaluating, deploying, and scaling AI applications. Key Responsibilities: Design and develop production-ready GenAI and ML applications. Build LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, Meta, Mistral, or open-source models. Develop RAG pipelines including document ingestion, chunking, embeddings, retrieval, reranking, and response generation. Build intelligent AI agents and agentic workflows using frameworks such as LangChain, LangGraph, or equivalent technologies. Develop prompt engineering, structured generation, tool calling, and function-calling solutions. Evaluate LLM/AI systems for accuracy, relevance, hallucination, latency, cost, and safety. Implement model fine-tuning techniques such as LoRA / QLoRA where appropriate. Integrate AI models with enterprise APIs, databases, search systems, and business applications. Develop scalable AI services and APIs using Python and FastAPI or equivalent frameworks. Work with Data Scientists and Data Engineers to integrate ML models and enterprise data. Deploy and operate AI solutions using cloud and container technologies. Implement observability, evaluation, monitoring, security, and responsible AI practices Required Skills: Strong Python and software engineering fundamentals. Hands-on experience with LLMs and Generative AI. Practical knowledge of RAG, embeddings, vector databases, prompt engineering, and LLM evaluation. Experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks. Understanding of machine learning and deep learning fundamentals. Experience building APIs and integrating AI models into applications. Knowledge of Git, Docker, CI/CD, and cloud deployment. Strong understanding of AI application architecture and production engineering. Good to Have: Experience with AI Agents, multi-agent systems, tool calling, MCP, or agent orchestration. Experience with OpenAI / Azure OpenAI, Anthropic, Gemini, Llama, Mistral or similar models. Experience with LoRA, QLoRA, Hugging Face, PyTorch. Knowledge of vector databases such as Pinecone, FAISS, Weaviate, or Milvus. Experience with MLflow, Azure ML, Databricks, Kubernetes. Experience with LLMOps, model evaluation, guardrails, observability, and AI security. Knowledge of multimodal AI, speech AI, or computer vision is an advantage. What We Look For Strong engineering fundamentals, hands-on AI implementation experience, experimentation mindset, and the ability to take an AI idea from prototype → production → scale Company Description: SYDIAI is an emerging AI and Data Science company focused on building intelligent decision systems for enterprises, particularly across commercial and supply chain functions. The company specializes in helping businesses make faster, data-driven decisions through advanced analytics, machine learning, and AI-powered optimization solutions. The company works on transforming fragmented enterprise operations into integrated, AI-driven decision ecosystems that improve efficiency, planning accuracy, operational visibility, and business outcomes. Their solutions combine deep domain expertise with applied AI, analytics, and optimization models. SYDIAI operates in the growing Enterprise AI, Supply Chain Analytics, and Decision Intelligence space, competing indirectly with firms working in: AI & Advanced Analytics Supply Chain Tech Enterprise Planning Platforms Data Science Consulting & Optimization Solutions The company appears to be positioned as a niche, high-impact AI solutions player focused on operational and supply chain intelligence.

Responsibilities

• : Design and develop production-ready GenAI and ML applications.
• Build LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, Meta, Mistral, or open-source models.
• Develop RAG pipelines including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
• Build intelligent AI agents and agentic workflows using frameworks such as LangChain, LangGraph, or equivalent technologies.
• Develop prompt engineering, structured generation, tool calling, and function-calling solutions.
• Evaluate LLM/AI systems for accuracy, relevance, hallucination, latency, cost, and safety.
• Implement model fine-tuning techniques such as LoRA / QLoRA where appropriate.
• Integrate AI models with enterprise APIs, databases, search systems, and business applications.