
AI Engineer
2 weeks ago
Key Responsibilities
- Design and implement
end-to-end data pipelines
for training and fine-tuning LLMs, including dataset creation, cleaning, augmentation, and labeling workflows. - Apply
advanced RAG techniques, prompt engineering, and model fine-tuning
(LoRA, PEFT, adapters) for domain-specific use cases. - Integrate AI models with
backend and frontend systems
via APIs, batching, caching, and streaming responses. - Deploy and optimize
LLMs & embeddings
using APIs and/or open-source models (OpenAI, Anthropic, LLaMA-family, Mistral, etc.). - Develop and maintain
secure AI APIs
using FastAPI/gRPC with Kubernetes and CI/CD pipelines. - Implement
safety and compliance layers
including prompt injection defenses, hallucination reduction, and PII redaction. - Collaborate with MLOps and platform engineering teams to ensure scalable deployment using
Docker, Kubernetes, and Ray/Serve
. - Leverage frameworks such as
LangChain, Hugging Face Transformers, and Azure OpenAI
for model orchestration and integration. - Work with
vector databases
(FAISS, Pinecone, Milvus) to build efficient retrieval-augmented generation pipelines.
Mandatory Skills
- Strong experience with
LLMs, embeddings, and fine-tuning techniques
. - Proficiency in
Python
and experience in
LangChain/Hugging Face
. - Hands-on experience with
MLOps (Docker, Kubernetes, CI/CD)
. - Strong knowledge of
model integration and scalable API development
. - Familiarity with
safety and compliance mechanisms in AI systems
.
Desirable / Plus
- Exposure to
speech-to-text models
(e.g., Whisper) with emphasis on Indian languages. - Prior experience in
NBFC / BFSI domain
.
-
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