Lead AI Application Engineer

We are seeking a highly experienced and visionary Lead AI Application Engineer to join our core AI team. This pivotal role will be instrumental in designing, developing, and deploying advanced Machine Learning, Generative AI and Agentic AI applications across high-throughput, real-time streaming environments. You are an engineer's engineer, someone who spent years training, tuning, and deploying models at scale, and who has spearheaded Generative and Agentic AI over the last 3-4 years. This is an exceptional opportunity for an individual who thrives on building sophisticated, production-grade Autonomous AI systems and wants to make a significant impact.

Key Responsibilities:

• Architect and operationalize end-to-end autonomous agentic systems (multi-agent orchestration, dynamic tool-calling, reflection, planner-executor loops, and persistent state management).
• Design robust LLM runtime infrastructure, including hybrid retrieval architectures (Advanced RAG, graph-based context grounding, vector databases, re-rankers, semantic routing).
• Establish scalable evaluation, guardrail, and observability frameworks (latency optimization, cost governors, hallucinations/drift detection, alignment checks, red-teaming).
• Make high-stakes architectural decisions regarding model topologies (proprietary frontier models vs. distilled/fine-tuned SLMs, LoRA, quantization, self-hosted vs. managed endpoints).
• Lead from the front: write high-leverage production code, design critical framework primitives, and rapidly de-risk zero-to-one concepts via functional PoCs.
• Bridge cutting-edge research and production engineering, ensuring systems are resilient, fault-tolerant, secure, and performant at scale.
• Partner directly with the Head of AI, Product leadership, and VP-level stakeholders to chart the AI technology roadmap and identify transformative capability unlocks.
• Lead the exploration and application of Large Language Models, Agentic AI and Generative AI, venturing into new areas within these fields.
• Represent the company's AI thought leadership through open-source contributions, technical papers, conferences, or industry forums.
• Architect, design and build robust, scalable, and efficient AI systems leveraging a variety of AI/ML, data science, deep learning, and agentic AI frameworks.
• Drive the integration of complex Model Context Protocol (MCP) and agent-to-agent (A2A) communication protocols, ensuring seamless interaction and optimal performance.
• Design and implement scalable and efficient AI/ML systems, architectures, and pipelines that can handle large volumes of data and deliver accurate and timely results.
• Regularly self-educate while mentoring and provide technical leadership to other engineers, fostering a culture of continuous learning and excellence.

Who we are looking for -:

• Experience: 15+ years of hands-on software development experience, including 5+ years actively building and deploying Generative AI and Agentic AI applications in production.
• Core Engineering & AI Building: Hands-on builder capable of independently architecting, coding, and deploying custom AI tools, autonomous agents, and bots from scratch.
• Programming Mastery: 8+ years writing production-grade code in Python or similar languages with deep expertise in scalable software design, APIs, and microservices.
• Agentic Frameworks & Tools: Deep proficiency with frameworks such as LangChain, LlamaIndex, Auto-GPT, CrewAI, and Model Context Protocol (MCP) for multi-agent coordination.
• LLMs & Fine-Tuning: Practical experience with frontier models (GPT-4, Gemini, Claude), fine-tuning, prompt engineering, and custom agentic tool-calling workflows.
• ML/DL Foundations: Background in training, tuning, and running models using PyTorch, TensorFlow, or Scikit-learn in high-throughput environments.
• Inference & Acceleration: Familiarity with inference engines (vLLM, TensorRT-LLM, TGI) and cloud ML infrastructure (NVIDIA GPUs, TPUs).
• Data & Memory Infrastructure: Experience building low-latency vector search indices, in-memory caching layers, streaming pipelines (Kafka/Flink), and NoSQL datastores to power real-time agent context and memory.
• Education: Master's or Ph.D. in Computer Science, AI, ML, or a related quantitative field (or equivalent practical experience).

Knowledge, Skills, and Abilities:

• Autonomous Execution: Proven track record as a senior IC who takes total ownership-from zero-to-one prototyping to production rollout.
• Cloud & DevOps: Exposure to AWS, Azure, or GCP environments with modern containerized deployment (Kubernetes, Docker).
• Domain & Communication: Experience in contact center/telecom/CX platforms is a plus; excellent technical communication skills required.

Experience 12+ Years of Experience

Education Bachelor degree or equivalent experience

Advance Degree preferred in Computer Science or Related Field

Skills

Auto-GPT, Claude, CrewAI, GPT-4, LangChain, LlamaIndex, MCP, PyTorch, Python, Scikit-learn, TensorFlow