Job Title: AI Engineer
Job Location: Remote
Job Type: Full time
We are seeking an innovative and hands-on AI Engineer to drive the design, development, and deployment of next-generation AI solutions. The ideal candidate will lead the creation of Agentic AI automation agents, develop proof of concepts (PoCs) and minimum viable products (MVPs), and collaborate closely with business and technology stakeholders to turn AI innovation into measurable business value.
Key Responsibilities:
Actively contribute to end-to-end development of PoCs / MVPs using Generative AI, large language models (LLMs), RAG, MCP development, and AI Engineering principles.
Design, build, and deploy agentic AI capable of autonomous decision-making, task orchestration, and workflow automation.
Collaborate with product, data, and engineering teams to identify high-impact AI use cases and define technical implementation paths.
Integrate GenAI models (e.g., OpenAI GPT, Claude, Gemini, Llama) and frameworks (e.g., LangChain, AutoGen, CrewAI) into enterprise products and services.
Foster alignment with stakeholders by converting technical innovation into actionable business outcomes.
Stay current with AI research and tools to continuously enhance solution capabilities.
Required Qualifications:
Bachelor s or Master s degree in Computer Science, Engineering, Data Science, or a related discipline.
7+ years of overall experience in software engineering or ML/AI solution delivery.
Proven hands-on experience in developing and deploying Generative AI models and AI agent frameworks on Production / Enterprise scale
Proficiency in Python and experience with AI/ML libraries such as PyTorch, TensorFlow, Hugging Face, and LangChain.
Familiarity with cloud platforms (AWS, Azure, Google Cloud Platform) and MLOps or AIOps practices.
Strong stakeholder management, communication, and leadership skills.
Ability to translate complex technical concepts into clear business insights and value propositions.
Preferred Qualifications:
Experience with multi-agent orchestration tools (e.g., CrewAI, AutoGen, or similar).
Exposure to reinforcement learning, prompt engineering, and memory architectures for intelligent agents.
Background in building end-to-end intelligent automation workflows using AI orchestration platforms.
Knowledge of data privacy, security, and governance in AI applications.
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