Description
Key Responsibilities :
Application Development
- Design and develop GenAI-enabled microservices and APIs using Python (FastAPI, Flask, Django).
- Implement LLM integrations using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or CrewAI.
- Build and manage RAG pipelines combining embeddings, vector stores, and retrieval mechanisms.
- Develop prompt templates and implement prompt chaining/orchestration for conversational AI systems.
Model & Data Integration
- Integrate models via APIs from OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or Hugging Face.
- Preprocess and vectorize text data using transformers, sentence embeddings, or custom tokenizers.
- Work with vector databases such as Pinecone, FAISS, Milvus, Weaviate, or Amazon OpenSearch.
- Collaborate with data engineers to connect structured and unstructured datasets (S3, Cosmos DB, SQL, etc.
Cloud & Deployment
- Containerize GenAI services using Docker and deploy on Kubernetes (EKS/AKS).
- Automate deployment pipelines using GitHub Actions, Azure DevOps, or Terraform.
- Implement logging, monitoring, and model telemetry using CloudWatch, Application Insights, or Prometheus.
- Ensure secure API management and key handling via AWS Secrets Manager or Azure Key Vault.
Optimization & Scalability
- Optimize inference latency and cost for LLM-based applications.
- Implement caching, batching, and token management strategies.
- Fine-tune or adapt models for domain-specific applications.
- Apply AI guardrails and output moderation for responsible AI use.
Required Skills & Qualifications
Core Technical Skills :
Strong expertise in Python (6+ years) with knowledge of :
- Async programming, REST APIs, and design patterns.
- Libraries : FastAPI, Flask, Pydantic, SQLAlchemy, Pandas, NumPy.
Proficiency In AI/GenAI SDKs And APIs
- OpenAI / Azure OpenAI / AWS Bedrock / Hugging Face Transformers.
Familiarity With LLM Orchestration
- LangChain, LlamaIndex, Semantic Kernel, CrewAI, or Dust.
- Understanding of embeddings, vector databases, and RAG workflows.
Cloud & DevOps Skills
Strong hands-on experience in AWS or Azure :
- AWS : Lambda, ECS/EKS, S3, SageMaker, Bedrock.
- Azure : Azure OpenAI, Azure ML, Cognitive Services, AKS.
- Experience with CI/CD, IaC (Terraform/Bicep), and API gateways.
- Knowledge of containerization and cloud security best practices.
AI & ML Fundamentals
NLP, embeddings, and tokenization.
Prompt engineering and few-shot learning techniques.
Model performance metrics (latency, perplexity, accuracy)
(ref:hirist.tech)
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