AI Engineer for Bangalore

• LLM Application & Agent Development: Design, build, and optimize sophisticated applications, intelligent AI agents, and systems powered by Large Language Models.
• Advanced Prompt Engineering & Optimization: Develop, test, iterate, and refine advanced prompt engineering techniques to elicit desired behaviours, ensure reliability, and maximize performance from LLMs for various complex tasks.
• LLM Fine-Tuning & Customization: Lead efforts in fine-tuning pre-trained LLMs on domain-specific datasets to enhance their capabilities and align them with specific business needs.
• LLM Evaluation & Benchmarking: Establish and implement rigorous evaluation frameworks, metrics, and processes to assess LLM performance, accuracy, fairness, safety, and robustness.
• Framework Utilization (Langchain/LangGraph): Architect and develop complex LLM-driven workflows, chains, multi-agent systems, and graphs using frameworks like Langchain and LangGraph.
• Cross-Functional Collaboration: Collaborate closely with Principal Architects (including those based internationally), data scientists, software engineers, and product teams to integrate LLM-based solutions into new and existing products and services.
• Performance, Scalability & Cost Optimization: Optimize LLM inference speed, throughput, scalability, and cost-effectiveness for production environments.
• Stay Current with LLM Advancements: Continuously research, evaluate, and experiment with the latest LLM architectures, open-source models, prompt engineering methodologies, agentic AI patterns, fine-tuning methods, and ethical AI considerations.
• LLMOps & Governance: Contribute to building and maintaining LLMOps infrastructure, including model versioning, monitoring, feedback loops, data management for fine-tuning, and governance for LLM deployments.
• API & Service Development: Develop robust APIs and microservices to serve LLM-based applications and agents reliably and at scale.
• Documentation & Knowledge Sharing: Create comprehensive technical documentation, share expertise on LLM and agent development best practices, and present findings to both technical and non-technical stakeholders.

• Educational Background: Bachelor’s or master’s degree in computer science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a closely related technical field.
• Professional Experience: 5-7 years of progressive experience in software development, with a minimum of 3+ years dedicated to AI development, including substantial hands-on experience in designing, building, and deploying LLM-based systems and AI agents.
• Programming Proficiency: Expert proficiency in Python and its ecosystem relevant to AI and LLMs.
• LLM, NLP & Agent Expertise: Deep understanding of Natural Language Processing (NLP) concepts, Transformer architectures, the inner workings of Large Language Models, and principles of AI agent design.
• LLM Frameworks & Tools: Significant hands-on experience with LLM-specific libraries and frameworks such as Hugging Face Transformers, Langchain, LangGraph, LlamaIndex, and similar tools for building LLM applications and agents.
• Cloud Platform Experience: Solid experience with one or more major cloud platforms (AWS, GCP, Azure) and their respective AI/ML services, particularly those for deploying and managing LLMs (e.g., Amazon Bedrock, Google Vertex AI, Azure OpenAI Service).
• Fine-Tuning & Evaluation Experience: Demonstrable experience in fine-tuning LLMs and implementing robust evaluation strategies for both models and agent performance.
• MLOps/LLMOps Practices: Experience with MLOps principles and tools, adapted for the LLM lifecycle (e.g., experiment tracking, model registries, CI/CD for LLMs and agent-based systems).
• Data Handling for LLMs: Understanding of data preprocessing, augmentation, and management techniques for training and fine-tuning LLMs.
• Version Control: Proficiency with Git and collaborative development workflows.

• Advanced LLM Architectures & Prompt Engineering: Deep experience with various LLM architectures, their trade-offs, and mastery of advanced prompt engineering techniques.
• Autonomous Agent & Multi-Agent Systems: Proven experience in designing, developing, and deploying autonomous AI agents or complex multi-agent systems.
• Vector Databases: Familiarity with vector databases (e.g., Pinecone, Weaviate, Milvus, Chroma) for retrieval augmented generation (RAG) and semantic search in agentic architectures.
• Distributed Systems for LLMs: Knowledge of distributed training and inference techniques for very large models.
• Ethical AI & Responsible LLM/Agent Development: Strong understanding of ethical considerations, bias detection, and responsible AI practices in the context of LLMs and AI agents.
• Research & Publications: Contributions to LLM or AI agent research, publications in relevant conferences/journals, or active participation in open-source LLM/agent projects.
• Domain-Specific LLM/Agent Applications: Experience applying LLMs and agents to solve problems in specific industry domains.
• Cloud Certifications: Relevant cloud certifications (e.g., AWS Certified Machine Learning, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate or similar MCP credentials).

• Programming: Python (expert), SQL.
• LLM/NLP/Agent Frameworks: Hugging Face Transformers, Langchain, LangGraph, LlamaIndex, PyTorch, TensorFlow, frameworks for agent development.
• Cloud Platforms & LLM Services: AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Machine Learning, Azure OpenAI Service).
• Tools: Docker, Kubernetes, MLflow, Weights & Biases, Vector Databases (e.g., Pinecone, Weaviate).
• Databases: Relational (SQL Server, PostgreSQL, MySQL), NoSQL (MongoDB), and Vector Databases.

• Exceptional analytical, creative, and critical thinking skills with a talent for innovative problem-solving in the generative AI and intelligent agent space.
• Outstanding communication skills, with the ability to explain complex LLM concepts and agent system designs to diverse audiences.
• Proven ability to work effectively both independently and as a key contributor in collaborative, agile teams.
• Meticulous attention to detail, especially regarding data quality, model behavior, agent reliability, and system robustness.
• A proactive, highly adaptable mindset with an insatiable curiosity and passion for the rapidly evolving field of Large Language Models and AI agents.

Recex