Technical Lead

About Catalytics Datum

Catalytics 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 Summary

We 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. Youll balance coding with guiding a small team, setting engineering standards, and ensuring reliable production deployments.

Skills & Competencies

Core 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 services

AI / 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 PyTorch

Agent Systems

Mandatory: Experience designing task workflows, tool calling, orchestration, error handling, and retries

Good-to-Have: Multi-agent architectures, HTN/graph-based planning, agent registries

Data Handling

Mandatory: SQL basics, working with structured & unstructured data, document processing

Good-to-Have: Vector databases (FAISS, Pinecone, Chroma), hybrid search

System Design

Mandatory: Designing scalable APIs, async processing, state management for agents, reliability patterns

Good-to-Have: Event-driven systems, message queues, distributed tracing

Model Deployment

Mandatory: Deploying AI services, monitoring latency, errors, and costs

Good-to-Have: MLOps for GenAI (MLflow), A/B testing LLM variants, canary releases

Cloud & Tools

Mandatory: Experience with Azure/AWS/GCP, OpenAI/Azure OpenAI, Hugging Face, LangChain/LlamaIndex

Good-to-Have: Private model hosting, GPU optimization, Microsoft Fabric

Domain Knowledge

Mandatory: Applying AI agents to real workflows (support automation, analytics assistants, ops workflows)

Good-to-Have: Cross-domain agent frameworks and reusable accelerators

Soft Skills (Leadership)

Mandatory: End-to-end ownership of delivery; stakeholder communication; breaking ambiguous problems into executable tasks; mentoring 25 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 & standards

Key Responsibilities

• Own end-to-end delivery of AI agent solutions design, build, test, deploy
• Lead a small team (25 engineers): task breakdown, code reviews, mentoring, delivery tracking
• Architect 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 control
• Act as the communication bridge between product, business, and engineering
• Set coding standards, architecture patterns, and best practices
• Identify risks (hallucinations, security, data leakage) and implement mitigations

Minimum Qualifications

• 35 years of software engineering / AI engineering experience
• Hands-on experience building LLM/GenAI features or intelligent automation
• Strong Python backend development experience
• Experience owning delivery of features from design to production
• Comfortable mentoring juniors and leading small teams
• Experience working with at least one cloud platform (Azure/AWS/GCP)

Nice-to-Have Qualifications

• Experience with multi-agent systems in production
• Exposure to RAG, vector databases, and agent observability
• Prior experience in fast-paced product or startup environments
• Open-source contributions in AI tooling

What Success Looks Like (First 612 Months)

• AI agent workflows live in production with measurable business impact
• Team delivering predictably with clean architecture and low rework
• Reduced failure rates (hallucinations, broken workflows, retries)
• Stakeholders trust delivery timelines and technical decisions
• Reusable agent patterns adopted across teams