Generative AI & LLM Application Engineer
Fortune #3
Experience
2 - 6 Yrs
Salary CTC
₹ 24 - 50 Lacs PA
Location
Remote (Fully Remote)
Job Type
Full-time
3 Openings
Deadline: Oct 12, 2026
Posted 1 week ago
Job Overview & Description
Join our AI Innovation Lab to build state-of-the-art Generative AI applications, Retrieval-Augmented Generation (RAG) pipelines, and intelligent agentic workflows that automate complex enterprise operations.
About Apple:
Apple is a leading global enterprise operating in Consumer Tech & Hardware. Partnered with MockAttempt Academy for specialized technical talent sourcing, campus drives, and 100% placement assurance pipelines.
Our hiring process for this role is integrated with MockAttempt Academy's verified talent benchmark.
About Apple:
Apple is a leading global enterprise operating in Consumer Tech & Hardware. Partnered with MockAttempt Academy for specialized technical talent sourcing, campus drives, and 100% placement assurance pipelines.
Our hiring process for this role is integrated with MockAttempt Academy's verified talent benchmark.
Key Responsibilities
- Design and deploy production RAG pipelines using LangChain, vector databases (Pinecone/Milvus), and custom embeddings.
- Fine-tune open-weight LLMs (Llama 3, Mistral) for domain-specific tasks using LoRA/QLoRA.
- Build async microservices with FastAPI for model inferencing with sub-100ms latency.
- Implement guardrails, hallucination evaluations, and cost-optimization token strategies.
- Fine-tune open-weight LLMs (Llama 3, Mistral) for domain-specific tasks using LoRA/QLoRA.
- Build async microservices with FastAPI for model inferencing with sub-100ms latency.
- Implement guardrails, hallucination evaluations, and cost-optimization token strategies.
Requirements & Qualifications
- Strong Python development experience and familiarity with PyTorch or Hugging Face transformers.
- Hands-on experience building LLM pipelines, prompt engineering, and embeddings.
- Solid grasp of vector indexes, caching strategies, and cloud deployment on AWS/GCP.
- Hands-on experience building LLM pipelines, prompt engineering, and embeddings.
- Solid grasp of vector indexes, caching strategies, and cloud deployment on AWS/GCP.
Required & Preferred Skills
Python
Mandatory / Must Have
Min Score: 85%
FastAPI
Preferred / Strongly Desired
Min Score: 85%
AWS
Preferred / Strongly Desired
Min Score: 85%
Redis
Preferred / Strongly Desired
Min Score: 85%
Generative AI
Mandatory / Must Have
Min Score: 85%
PyTorch
Preferred / Strongly Desired
Min Score: 85%
LangChain
Mandatory / Must Have
Min Score: 85%
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