Job Title

We’re Hiring Job title: Senior AI EngineerLocation: BangaloreExperience: 5+ YearsNotice Period: Immediate- 15 DaysAI ENGINEER - SENIOR LEVELPosition Overview We are seeking a Senior AI Engineer to lead the design, development, and deployment of advanced Generative AI systems, including sophisticated multi-agent workflows, production-grade RAG implementations, and enterprise-scale AI applications. This role requires deep technical expertise combined with the ability to mentor team members and drive architectural decisions. Must-Have Skills & Experience Experience Requirements: 5-8 years of professional experience in AI/ML Engineering, Data Science, or Software Engineering with AI focus Proven track record of delivering 5+ production AI systems from conception to deployment Experience leading technical workstreams or mentoring junior engineers Demonstrated ability to troubleshoot complex AI system failures and performance issues Core Technical Skills: Advanced Python: Expert-level Python with strong software engineering fundamentals (design patterns, SOLID principles, testing) LLM Orchestration: Deep expertise in LangChain, LangGraph, and at least one other framework (LlamaIndex, Haystack) Agentic AI: Hands-on experience building multi-agent systems with planning, reasoning, tool-use, and memory capabilities Advanced RAG: Expertise in retrieval optimization including: Embedding model selection and comparison Hybrid search strategies (dense + sparse retrieval) Re-ranking techniques (Cohere, ColBERT, cross-encoders) Query reformulation and expansion Metadata filtering and structured retrieval Vector Databases: Production experience with vector database optimization, indexing strategies (HNSW, IVF), and performance tuning Cloud Platforms: Strong experience deploying and scaling AI workloads on Azure, AWS, or GCP Semantic caching implementation Synthetic data generation for training/evaluation Specific foundation model expertise (GPT-4, Claude, Gemini, Llama) Guardrails and safety frameworks Agent Architecture: Expert knowledge of agent orchestration patterns including state machines, ReAct, and planning frameworks Experience implementing scratchpad reasoning and chain-of-thought prompting Knowledge of tool routing, dynamic tool selection, and API orchestration Experience building memory systems (short-term, long-term, episodic) System Design & MLOps: Experience designing scalable AI architectures for enterprise applications Strong understanding of observability, logging, and tracing for AI systems (LangSmith, LangFuse, Weights & Biases) Knowledge of prompt versioning and evaluation pipelines Experience with CI/CD for ML systems Understanding of cost optimization strategies for LLM applications Data Engineering: Experience building data pipelines for AI applications Knowledge of data preprocessing, transformation, and quality assurance Familiarity with both SQL and NoSQL databases Good-to-Have Skills Multi-Agent Expertise: Production experience with LangGraph, CrewAI, or AutoGen for multi-agent orchestration Knowledge of agent communication protocols and coordination patterns Experience with hierarchical agent structures and delegation patterns Advanced AI Techniques: Experience with fine-tuning foundation models (LoRA, QLoRA, full fine-tuning) Knowledge of model quantization and optimization techniques Familiarity with function calling and structured output parsing Experience with streaming and real-time AI applications Evaluation & Testing: Expertise in LLM evaluation frameworks (RAGAS, TruLens, UpTrain) Experience designing golden test sets and benchmark suites Knowledge of human-in-the-loop evaluation methodologies Experience with A/B testing and experimentation frameworks Enterprise AI: Deep understanding of AI governance, compliance, and responsible AI Experience implementing security controls (PII redaction, access controls, audit logging) Knowledge of enterprise architecture patterns and integration strategies Familiarity with on-premises deployment and air-gapped environments Document Intelligence: Advanced experience with document parsing, OCR (Azure Document Intelligence, Textract) Knowledge of layout-aware chunking and document understanding Experience with table extraction and multimodal document processing Certifications: Azure AI Engineer Associate or Expert AWS Certified Machine Learning - Specialty Google Cloud Professional ML Engineer Certified Kubernetes Application Developer (CKAD) - bonus Domain Expertise: Experience with finance, accounting, ERP systems, or healthcare applications Industry-specific AI application experience Key Responsibilities Lead end-to-end ownership of AI feature streams from design to production Design and implement sophisticated multi-agent workflows with complex orchestration logic Build evaluation frameworks and establish quality benchmarks for AI systems Troubleshoot production issues and optimize system performance (latency, cost, accuracy) Mentor mid-level and junior engineers through code reviews and pair programming Collaborate with architects and product teams on technical roadmaps Create technical documentation, runbooks, and knowledge transfer materials Drive best practices for prompt engineering, testing, and deployment Deliverables Production-grade multi-agent systems with comprehensive error handling and recovery Evaluation harnesses with automated regression testing Performance optimization reports (latency benchmarks, cost analysis) Technical architecture documents and system design specifications Mentorship and knowledge transfer sessions for team members Educational Requirements Bachelor's degree in Computer Science, Engineering, Mathematics, or related field Master's degree preferred OR equivalent experience with strong portfolio of AI projects Soft Skills Excellent problem-solving and debugging skills Strong communication abilities - can explain complex systems to both technical and non-technical audiences Leadership qualities with experience guiding technical discussions Ability to make pragmatic trade-offs between perfection and delivery Proactive approach to identifying and resolving technical debt Collaborative mindset with focus on team success