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.