{"id":1230188,"url":"https://alion.io/job/whitetable-technical-lead","title":"Technical Lead","company":{"id":3800729,"name":"Whitetable","domain":"whitetable.ai","url":"https://alion.io/company/whitetable-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":54},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Angular","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Express","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Jenkins","optional":false},{"name":"LLM","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Rest API","optional":false},{"name":"Weaviate","optional":false},{"name":"Fine-tuning","optional":true},{"name":"JavaScript","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-09-21T04:39:27Z","employer_posted_date":null,"last_verified_at":"2026-09-21T04:39:27Z","board_verified":false,"closed_at":null,"days_open":16,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":16},"description":"Tech Lead - Engineering & AI\n\nAbout the Role : \n\nWe are looking for a highly capable and hands-on Tech Lead - Engineering & AI to lead the architecture, development, and deployment of scalable software and AI-driven products.\n\nThe role combines technical leadership, full-stack engineering, AI/LLM engineering, system architecture, DevOps/MLOps, and team mentorship. The ideal candidate will be comfortable working across both conventional software systems and modern AI architectures, including RAG pipelines, agentic workflows, LLM-powered applications, and AI production infrastructure.\n\nYou will play a key role in shaping technical direction, driving engineering excellence, and taking products and AI capabilities from concept and architecture through production deployment and continuous optimization.\n\nKey Responsibilities : \n\nTechnical Leadership & Architecture : \n\n- Lead the design and implementation of scalable, reliable, and maintainable software architectures.\n\n- Architect solutions across the MERN/MEAN ecosystem and modern AI/ML systems.\n\n- Design and review architectures involving RAG pipelines, agentic workflows, LLM-powered applications, and AI-driven products.\n\n- Identify architectural bottlenecks and drive improvements in system reliability, scalability, security, and performance.\n\n- Establish engineering best practices around coding standards, architecture, testing, and technical documentation.\n\nTeam Leadership & Mentorship : \n\n- Lead and mentor a team of software engineers and AI/ML engineers.\n\n- Conduct code reviews, architecture reviews, and prompt/evaluation reviews.\n\n- Provide technical guidance and support engineers in solving complex development and production challenges.\n\n- Break down complex technical requirements into actionable tasks and ensure effective execution.\n\n- Foster a culture of ownership, technical excellence, collaboration, and continuous learning.\n\nAI & LLM Engineering : \n\n- Design and develop production-grade applications powered by LLMs and generative AI.\n\n- Build and optimize RAG pipelines, agentic systems, LLM workflows, and AI-driven analytics solutions.\n\n- Work with LLM platforms and APIs such as OpenAI, Anthropic, Gemini, and open-source models.\n\n- Design multi-model and multi-tier orchestration strategies based on performance, cost, latency, and accuracy requirements.\n\n- Implement evaluation frameworks to measure and continuously improve AI system quality.\n\n- Optimize prompts, retrieval strategies, model selection, token usage, and inference performance.\n\nDevOps, MLOps & Infrastructure : \n\n- Own and improve CI/CD pipelines, infrastructure, and production deployment processes.\n\n- Design and manage deployment pipelines for both conventional applications and AI/ML workloads.\n\n- Work with containerized environments and cloud infrastructure across AWS, Azure, or GCP.\n\n- Manage infrastructure supporting model inference, vector databases, APIs, and AI workloads.\n\n- Implement appropriate monitoring, logging, observability, and reliability practices.\n\n- Evaluate and implement serverless and scalable infrastructure patterns where appropriate.\n\nRelease & Delivery Management : \n\n- Drive the complete software and AI feature release lifecycle from development through production.\n\n- Coordinate with engineering, product, and other stakeholders to ensure timely and high-quality releases.\n\n- Establish effective release processes, deployment standards, and rollback strategies.\n\n- Identify and proactively address technical risks that may impact delivery timelines or production stability.\n\nHands-on Engineering : \n\n- Remain hands-on with development while providing technical leadership.\n\n- Contribute directly to solving complex architectural, backend, AI/ML, and infrastructure problems.\n\n- Debug production issues across application, infrastructure, and AI/LLM layers.\n\n- Improve system performance, retrieval quality, model behavior, and application reliability.\n\nCost & Performance Optimization : \n\n- Own the cost-performance trade-offs associated with production AI systems.\n\n- Optimize LLM selection, token consumption, prompt efficiency, inference costs, and latency.\n\n- Monitor AI infrastructure and API costs and identify opportunities for optimization.\n\n- Balance system accuracy, scalability, latency, reliability, and operating costs.\n\nTechnical Requirements : \n\nCore Engineering : \n\n- 4+ years of professional software development experience.\n\n- At least 1+ year of experience in a technical leadership or senior engineering capacity.\n\n- Strong hands-on expertise in Python and Node.js.\n\n- Strong experience with at least one modern frontend framework such as React or Angular.\n\n- Strong working knowledge of Express.js, PostgreSQL, and MongoDB.\n\n- Strong understanding of REST APIs, database design, distributed systems, and system architecture.\n\nAI / LLM Engineering : \n\n- Hands-on experience building and deploying LLM-powered applications.\n\n- Strong experience with LLM APIs/platforms such as OpenAI, Anthropic, Gemini, or open-source models.\n\n- Experience with RAG architectures, Vector databases, Agentic workflows, Prompt engineering, LLM evaluation, Model orchestration, and AI/ML production systems.\n\n- Experience with vector databases such as pgvector, Pinecone, Weaviate, or equivalent technologies.\n\n- Understanding of LLM latency, accuracy, scalability, and cost trade-offs.\n\nInfrastructure & DevOps : \n\n- Strong understanding of CI/CD pipelines using tools such as Jenkins, GitHub Actions, or GitLab CI.\n\n- Experience with Docker/containerization and cloud deployment.\n\n- Working knowledge of AWS, Azure, or GCP.\n\n- Understanding of MLOps concepts, model deployment, inference infrastructure, monitoring, and observability.\n\n- Familiarity with serverless architectures and cloud-native patterns is a plus.\n\nProject Experience : \n\nCandidates should have successfully delivered 3+ significant projects end-to-end, covering multiple stages such as Architecture, Development, Testing, Deployment, Production, and Optimization.\n\nAt least some of these projects should involve meaningful AI/ML or LLM engineering, such as RAG-based applications, LLM-powered products, Agentic AI systems, AI-driven analytics, LLM fine-tuning, AI automation platforms, or production-grade generative AI systems.\n\nCandidates should be prepared to clearly explain their individual contribution, architectural decisions, technical challenges, and measurable outcomes for these projects.\n\nPreferred Attributes : \n\n- Strong problem-solving and analytical ability.\n\n- Ability to identify underlying architectural, engineering, or data issues rather than addressing only surface-level problems.\n\n- Strong communication and stakeholder management skills.\n\n- Ability to translate complex technical concepts into clear execution plans.\n\n- Strong ownership and bias toward execution.\n\n- Comfortable working in a fast-paced, highly collaborative environment.\n\n- Strong interest in emerging AI/LLM technologies and their practical application in production systems.\n\nLocation & Work Environment : \n\n- This is an in-office role based in Bengaluru.\n\n- Candidates currently based in Bengaluru or willing to relocate are preferred.\n\n- The role requires close collaboration with engineering and cross-functional teams.\n\nWhat You Can Expect : \n\n- Significant ownership over the technical direction of software and AI systems.\n\n- Opportunity to work across full-stack engineering, AI/LLM systems, architecture, and infrastructure.\n\n- Direct involvement in building and scaling production-grade AI products.\n\n- Opportunity to mentor engineers and influence engineering practices.\n\n- A fast-paced environment focused on technical excellence, ownership, and execution.\nSkills\nArtificial Intelligence, LLM, Python, RAG, MEAN, MERN Stack, Technical Architect, OpenAI, Node.js, Express.js","description_format":"text","description_chars":7849,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous 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