Technical Lead

About Catalytics DatumCatalytics Datum is the Next-Gen Enterprise that amalgamates Data Science, Big Data, Cloud Computing & Business Intelligence to solve complex business problems for enterprises through user experience and faster decision-making. Recognized by Microsoft BizSpark, Catalytics is present across the globe to become your partner in Digital Transformation.Catalytics Datum offers Platform as a Service, which is One Stop Solution. The complete process; starting from Requirement Gathering to the Final Deployment, is data-driven, processed by collaborative and different Predictive modeling tools which leave clients overhead free. We provide up to 99.9% accurate results in order to increase profitability by providing the deepest insights of your brands. Role SummaryWe are looking for a Tech Lead to build and lead a team developing AI agents for enterprise use cases (copilots, workflow automation, decision support, multi-agent systems). This is a hands-on leadership role where you will own delivery end-to-end — from technical design and development to stakeholder communication and people management. You’ll balance coding with guiding a small team, setting engineering standards, and ensuring reliable production deployments.Skills & CompetenciesCore Programming Mandatory: Strong Python for backend services (FastAPI), solid data structures, clean code practices, Git Good-to-Have: TypeScript/Node.js for frontend or API integration, Go/Java for high-throughput servicesAI / ML Fundamentals Mandatory: Experience building AI/LLM-powered features, prompt engineering, embeddings, basic RAG pipelines Good-to-Have: Fine-tuning LLMs (LoRA), multi-modal models, Transformers with PyTorchAgent Systems Mandatory: Experience designing task workflows, tool calling, orchestration, error handling, and retries Good-to-Have: Multi-agent architectures, HTN/graph-based planning, agent registriesData Handling Mandatory: SQL basics, working with structured & unstructured data, document processing Good-to-Have: Vector databases (FAISS, Pinecone, Chroma), hybrid searchSystem Design Mandatory: Designing scalable APIs, async processing, state management for agents, reliability patterns Good-to-Have: Event-driven systems, message queues, distributed tracingModel Deployment Mandatory: Deploying AI services, monitoring latency, errors, and costs Good-to-Have: MLOps for GenAI (MLflow), A/B testing LLM variants, canary releasesCloud & Tools Mandatory: Experience with Azure/AWS/GCP, OpenAI/Azure OpenAI, Hugging Face, LangChain/LlamaIndex Good-to-Have: Private model hosting, GPU optimization, Microsoft FabricDomain Knowledge Mandatory: Applying AI agents to real workflows (support automation, analytics assistants, ops workflows) Good-to-Have: Cross-domain agent frameworks and reusable acceleratorsSoft Skills (Leadership) Mandatory: End-to-end ownership of delivery; stakeholder communication; breaking ambiguous problems into executable tasks; mentoring 2–5 engineers; hands-on development where needed; presenting architecture, risks, and trade-offs clearly (PPT/demo) Good-to-Have: Executive communication; influencing roadmap; setting engineering culture & standardsKey ResponsibilitiesOwn end-to-end delivery of AI agent solutions — design, build, test, deployLead a small team (2–5 engineers): task breakdown, code reviews, mentoring, delivery trackingArchitect agent workflows (planning, tool use, retries, human-in-the-loop)Build LLM-powered APIs and orchestration services (hands-on when needed)Drive production readiness: observability, guardrails, cost & latency controlAct as the communication bridge between product, business, and engineeringSet coding standards, architecture patterns, and best practicesIdentify risks (hallucinations, security, data leakage) and implement mitigations Minimum Qualifications3–5 years of software engineering / AI engineering experienceHands-on experience building LLM/GenAI features or intelligent automationStrong Python backend development experienceExperience owning delivery of features from design to productionComfortable mentoring juniors and leading small teamsExperience working with at least one cloud platform (Azure/AWS/GCP) Nice-to-Have QualificationsExperience with multi-agent systems in productionExposure to RAG, vector databases, and agent observabilityPrior experience in fast-paced product or startup environmentsOpen-source contributions in AI tooling What Success Looks Like (First 6–12 Months)AI agent workflows live in production with measurable business impactTeam delivering predictably with clean architecture and low reworkReduced failure rates (hallucinations, broken workflows, retries)Stakeholders trust delivery timelines and technical decisionsReusable agent patterns adopted across teams

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