Technical Lead
First seen by Alion on Sep 14, 2026.
About the Role :
Learning Pathways is building an AI-native enterprise SaaS platform that benchmarks organizational skills, identifies capability gaps, and generates personalized learning pathways using LLMs, RAG, and conversational AI.
We are looking for a hands-on Technical Lead to own the technical design and engineering execution of the platform. Reporting to the Founders, you will lead a cross-functional pod of 5 - 7 engineers, translating user stories into robust technical solutions and bridging the gap between applied AI and highly scalable enterprise infrastructure.
What You'll Build :
- Skills intelligence and benchmarking engines
- Learning Pathways and Learning Mentor conversational interfaces
- Atlas Companion multi-agent system
- High-throughput AI/RAG pipelines
- Enterprise APIs, secure multi-tenant architecture, and data service
In Your First 90 Days, You Will :
- Audit our existing AI/RAG architecture and establish baselines for retrieval accuracy and inference latency.
- Standardize the CI/CD pipeline and deployment strategies for our core Python/FastAPI microservices.
- Architect and unblock the engineering team for the V1 launch of the Atlas Companion agent.
- Establish clear engineering cadences, code review standards, and technical documentation practices for your team.
Key Responsibilities :
1. Technical Design and Architecture :
- Translate user stories and product requirements into scalable technical designs.
- Define application architecture, APIs, and service boundaries for both traditional backends and AI agent workflows.
- Design database schemas and data models across PostgreSQL, MongoDB, and vector stores.
- Make pragmatic architectural decisions regarding cloud infrastructure (AWS/GCP), GPU provisioning, and Kubernetes deployments.
2. Hands-on Engineering and AI Integration :
- Write production-quality Python/FastAPI code for core backend services.
- Design and orchestrate multi-agent workflows using LangGraph and implement robust tool-calling using Model Context Protocol (MCP).
- Optimize model inference latency and throughput for conversational AI features using continuous batching frameworks like vLLM.
- Work with the Data Science team to translate AI/product concepts into scalable production systems.
3. Scalability and Security :
- Establish appropriate caching (Redis/Valkey), asynchronous processing, and worker patterns.
- Manage GPU memory allocation and inference scaling to prevent system bottlenecks.
- Ensure secure multi-tenant architecture, data isolation, RLS, and strict authentication/authorization patterns.
4. Engineering Leadership :
- Lead and mentor a growing engineering team of 5 to 7 backend and AI engineers.
- Break technical requirements into actionable engineering stories and support sprint estimations.
- Establish modern engineering practices around testing, CI/CD, MLFlow, and observability (LangSmith/Datadog).
Requirements :
1. Core Systems and Architecture :
- Backend : Deep expertise in Python and FastAPI.
- Databases : PostgreSQL, MongoDB, Redis/Valkey, and advanced database/query optimization.
- Infrastructure : Distributed systems, Docker, Kubernetes, and cloud-native development (AWS/GCP).
- Security : Strong grasp of AuthN/AuthZ, Row-Level Security, and enterprise data isolation.
2. Applied AI and ML Engineering :
- LLM Orchestration : Experience building production applications with LangChain, LangGraph, and LangSmith.
- Information Retrieval : Advanced RAG patterns, semantic/hybrid search, embedding models, and re-ranking architectures.
- Agentic Systems : Designing robust agent/tool architectures (MCP : Client and Server).
- Ecosystem : Familiarity with HuggingFace and the Transformers library.
3. Bonus / Nice-to-Haves :
- Node.js / TypeScript : Ability to navigate and contribute to existing Node.js services or front-end BFFs.
- Model Optimization : Experience with Supervised Fine-Tuning, specifically Parameter-Efficient Fine-Tuning (PEFT) like LoRA.
- High-Throughput Serving : Experience deploying and scaling open-weight models using vLLM.
What We're Looking For :
- 7+ years of software engineering experience, with significant time as a Technical Lead or Software Architect.
- Experience building production SaaS or enterprise platforms, specifically integrating AI/LLM components.
- Ability to move fluidly from user story - architecture - database - API - production.
- Thinks about scalability and security from the beginning, while knowing when to build for scale vs. keep things simple.
- Takes ownership of the technical outcome rather than just completing assigned tasks.
Compensation, Benefits and Culture :
- Work Setup : On-site in Hyderabad
- Benefits : Competitive pay, learning support
Equal Opportunity Statement :
Learning Pathways is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Skills
Artificial Intelligence, Machine Learning, LLM, RAG, Python, FastAPI, Application Architecture, PostgreSQL, Distributed Systems, LangGraph, LangChain