EverestDX - Python Developer - Generative AI

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 :

- Good understanding of :

NLP, embeddings, and tokenization.

Prompt engineering and few-shot learning techniques.

Model performance metrics (latency, perplexity, accuracy)