AI Engineer (RAG & Fine‑Tuning Specialist)
at Lalitech
5 - 10 yrs
₹7L - ₹20L / yr
Remote, Bengaluru (Bangalore), Noida
Skills
Artificial Intelligence (AI)
Generative AI
Python
NodeJS (Node.js)Vector database
Amazon Web Services (AWS)
DockerRetrieval Augmented Generation (RAG)
CI/CD
MongoDBAWS Lambda
Amazon EC2
Location: Hybrid/ Remote
Type: Contract / Full‑Time
Experience: 5+ Years
Qualification: Bachelor’s or Master’s in Computer Science or a related technical field
Responsibilities:
- Architect & implement the RAG pipeline: embeddings ingestion, vector search (MongoDB Atlas or similar), and context-aware chat generation.
- Design and build Python‑based services (FastAPI) for generating and updating embeddings.
- Host and apply LoRA/QLoRA adapters for per‑user fine‑tuning.
- Automate data pipelines to ingest daily user logs, chunk text, and upsert embeddings into the vector store.
- Develop Node.js/Express APIs that orchestrate embedding, retrieval, and LLM inference for real‑time chat.
- Manage vector index lifecycle and similarity metrics (cosine/dot‑product).
- Deploy and optimize on AWS (Lambda, EC2, SageMaker), containerization (Docker), and monitoring for latency, costs, and error rates.
- Collaborate with frontend engineers to define API contracts and demo endpoints.
- Document architecture diagrams, API specifications, and runbooks for future team onboarding.
Required Skills
- Strong Python expertise (FastAPI, async programming).
- Proficiency with Node.js and Express for API development.
- Experience with vector databases (MongoDB Atlas Vector Search, Pinecone, Weaviate) and similarity search.
- Familiarity with OpenAI’s APIs (embeddings, chat completions).
- Hands‑on with parameters‑efficient fine‑tuning (LoRA, QLoRA, PEFT/Hugging Face).
- Knowledge of LLM hosting best practices on AWS (EC2, Lambda, SageMaker).
Containerization skills (Docker):
- Good understanding of RAG architectures, prompt design, and memory management.
- Strong Git workflow and collaborative development practices (GitHub, CI/CD).
Nice‑to‑Have:
- Experience with Llama family models or other open‑source LLMs.
- Familiarity with MongoDB Atlas free tier and cluster management.
- Background in data engineering for streaming or batch processing.
- Knowledge of monitoring & observability tools (Prometheus, Grafana, CloudWatch).
- Frontend skills in React to prototype demo UIs.
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About Lalitech
Founded :
2018
Type :
Products & Services
Size :
20-100
Stage :
Profitable
About
We are a group of technology experts who love creating products. Our specialty lies in kickstarting innovations. Whether you are a big company or small, we work closely with you from inception to release. Our dedicated teams, spread across opposite time zones, can help quickly materialize your ideas, and empower you to go to market faster.
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