Job Title

Position OverviewWe 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 & ExperienceExperience Requirements:5-8 years of professional experience in AI/ML Engineering, Data Science, or Software Engineering with AI focusProven track record of delivering 5+ production AI systems from conception to deploymentExperience leading technical workstreams or mentoring junior engineersDemonstrated ability to troubleshoot complex AI system failures and performance issuesCore 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 capabilitiesAdvanced RAG: Expertise in retrieval optimization including:Embedding model selection and comparisonHybrid search strategies (dense + sparse retrieval)Re-ranking techniques (Cohere, ColBERT, cross-encoders)Query reformulation and expansionMetadata filtering and structured retrievalVector Databases: Production experience with vector database optimization, indexing strategies (HNSW, IVF), and performance tuningCloud Platforms: Strong experience deploying and scaling AI workloads on Azure, AWS, or GCPSemantic 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 frameworksExperience implementing scratchpad reasoning and chain-of-thought promptingKnowledge of tool routing, dynamic tool selection, and API orchestrationExperience building memory systems (short-term, long-term, episodic)System Design & MLOps:Experience designing scalable AI architectures for enterprise applicationsStrong understanding of observability, logging, and tracing for AI systems (LangSmith, LangFuse, Weights & Biases)Knowledge of prompt versioning and evaluation pipelinesExperience with CI/CD for ML systemsUnderstanding of cost optimization strategies for LLM applicationsData Engineering:Experience building data pipelines for AI applicationsKnowledge of data preprocessing, transformation, and quality assuranceFamiliarity with both SQL and NoSQL databasesGood-to-Have SkillsMulti-Agent Expertise:Production experience with LangGraph, CrewAI, or AutoGen for multi-agent orchestrationKnowledge of agent communication protocols and coordination patternsExperience with hierarchical agent structures and delegation patternsAdvanced AI Techniques:Experience with fine-tuning foundation models (LoRA, QLoRA, full fine-tuning)Knowledge of model quantization and optimization techniquesFamiliarity with function calling and structured output parsingExperience with streaming and real-time AI applicationsEvaluation & Testing:Expertise in LLM evaluation frameworks (RAGAS, TruLens, UpTrain)Experience designing golden test sets and benchmark suitesKnowledge of human-in-the-loop evaluation methodologiesExperience with A/B testing and experimentation frameworksEnterprise AI:Deep understanding of AI governance, compliance, and responsible AIExperience implementing security controls (PII redaction, access controls, audit logging)Knowledge of enterprise architecture patterns and integration strategiesFamiliarity with on-premises deployment and air-gapped environmentsDocument Intelligence:Advanced experience with document parsing, OCR (Azure Document Intelligence, Textract)Knowledge of layout-aware chunking and document understandingExperience with table extraction and multimodal document processingCertifications:Azure AI Engineer Associate or ExpertAWS Certified Machine Learning - SpecialtyGoogle Cloud Professional ML EngineerCertified Kubernetes Application Developer (CKAD) - bonusDomain Expertise:Experience with finance, accounting, ERP systems, or healthcare applicationsIndustry-specific AI application experienceKey ResponsibilitiesLead end-to-end ownership of AI feature streams from design to productionDesign and implement sophisticated multi-agent workflows with complex orchestration logicBuild evaluation frameworks and establish quality benchmarks for AI systemsTroubleshoot production issues and optimize system performance (latency, cost, accuracy)Mentor mid-level and junior engineers through code reviews and pair programmingCollaborate with architects and product teams on technical roadmapsCreate technical documentation, runbooks, and knowledge transfer materialsDrive best practices for prompt engineering, testing, and deploymentDeliverablesProduction-grade multi-agent systems with comprehensive error handling and recoveryEvaluation harnesses with automated regression testingPerformance optimization reports (latency benchmarks, cost analysis)Technical architecture documents and system design specificationsMentorship and knowledge transfer sessions for team membersEducational RequirementsBachelor's degree in computer science, Engineering, Mathematics, or related fieldMaster's degree preferred OR equivalent experience with strong portfolio of AI projectsSoft SkillsExcellent problem-solving and debugging skillsStrong communication abilities - can explain complex systems to both technical and non-technical audiencesLeadership qualities with experience guiding technical discussionsAbility to make pragmatic trade-offs between perfection and deliveryProactive approach to identifying and resolving technical debtCollaborative mindset with focus on team success