Position: Senior Agentic AI Developer / Practice Lead
Experience: 8–12 Years
Location: Hyderabad, India
Onboarding: Immediate or as per availability
Job Type: Full-Time
Work Mode: Work from Office
We are seeking a highly experienced Senior Agentic AI Developer / Practice Lead to design, build, and optimize advanced agent-based AI systems. This role is ideal for professionals with deep technical expertise in AI/ML, software development, and system integration, with proven experience in building autonomous AI agents capable of reasoning, memory, planning, and multi-step execution.
Responsibilities:
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- Lead the design and implementation of agentic AI frameworks with reasoning, memory, and reflection loops.
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- Integrate LLMs (GPT, Claude, LLaMA, Mistral) with external tools, APIs, and knowledge bases.
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- Architect scalable AI systems leveraging frameworks such as LangChain, LlamaIndex, Haystack, AutoGen, or DSPy.
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- Implement memory management (vector DBs, datastores) for agent persistence.
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- Optimize performance, scalability, and reliability of AI-driven workflows.
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- Connect agents with enterprise systems (CRM, ERP, cloud services, RPA tools) for autonomous task execution.
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- Lead a team of junior developers and AI engineers; provide mentorship and define best practices.
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- Stay updated on GenAI, agent frameworks, reinforcement learning, and multi-modal AI (text, speech, vision).
Qualifications:
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- Bachelor’s or Master’s degree in Computer Science, AI, or related field.
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- 8–12 years of overall experience in AI/ML and software development.
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- Hands-on experience building agentic AI systems.
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- Strong programming skills in Python, Node.js, or Java.
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- Expertise with LLMs and NLP pipelines (OpenAI, Anthropic, Cohere, Hugging Face).
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- Hands-on experience with frameworks like LangChain, AutoGen, DSPy, or CrewAI.
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- Proficiency with databases & vector stores (Pinecone, Weaviate, Milvus, FAISS, Redis).
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- Experience with cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes).
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- Knowledge of MLOps, CI/CD, API design, and microservices.
Desired Skills:
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- Familiarity with reinforcement learning, prompt engineering, and fine-tuning LLMs.
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- Ability to design autonomous task loops (Plan → Act → Reflect).
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- Contributions to open-source agentic AI frameworks.
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- Experience with multi-agent coordination in real-world use cases.
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- Knowledge of cybersecurity best practices in AI agent design.
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- Prior work on enterprise automation, RPA, or AI copilots.
Interview Process:
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- Fast-track: Interviews scheduled promptly upon receipt of profiles.
- Timely feedback: Rapid responses for smooth hiring and onboarding.