53 Llm Apis Jobs - Page 3

Role & responsibilities : Requirements: Strong expertise in NLP, text summarization, semantic search, and LLM APIs. Practical experience with Amazon Bedrock, OpenAI, or Hugging Face transformers. Familiar with prompt tuning and few-shot learning. Python (pandas, langchain, boto3, NumPy, etc.) Experience working with unstructured audio-to-text data (e.g., call transcripts). Key Responsibilities: Design and Development: Design, develop, and deploy LLM-based solutions for text summarization, semantic search, and other NLP tasks LLM APIs: Integrate LLM APIs from Amazon Bedrock, OpenAI, or Hugging Face transformers into existing applications Prompt Tuning and Few-Shot Learning: Implement prompt tuning and few-shot learning techniques to improve LLM performance Unstructured Audio-to-Text Data: Work with unstructured audio-to-text data, such as call transcripts, to develop accurate and efficient NLP models Python Programming: Utilize Python libraries like pandas, LangChain, boto3, and NumPy for data processing and model development Preferred candidate profile : We're seeking a highly skilled NLP Engineer with expertise in Large Language Models (LLMs) and text summarization to join our team. The ideal candidate will have hands-on experience with Amazon Bedrock, OpenAI, or Hugging Face transformers and a strong background in Python programming. This role involves working with unstructured audio-to-text data, such as call transcripts, and developing innovative solutions using LLMs.

The JD for AI/Ml programmer is given below: Key Responsibilities: Design, develop, and deploy Generative AI models using state-of-the-art architectures (e.g., Transformers, Diffusion models). Build and fine-tune LLM-powered agents capable of multi-step reasoning, task planning, and tool use. Work with frameworks like LangChain, AutoGPT, BabyAGI, CrewAI , or similar agent orchestration tools. Integrate models with REST APIs, vector databases (e.g., Pinecone, FAISS, Chroma), and external systems. Optimize inference pipelines for performance, latency, and scalability. Collaborate with product managers and data scientists to prototype and productionize AI features. Stay updated on recent advancements in Generative AI and autonomous agents. Required Qualifications: 34 years of hands-on experience in Machine Learning / Deep Learning , with at least 1–2 years in Generative AI and/or AI Agents . Proficiency in Python and ML libraries such as PyTorch , TensorFlow , Transformers (Hugging Face) . Experience with LLM APIs (OpenAI, Claude, Mistral, etc.) and building LLM-based applications. Solid understanding of prompt engineering , fine-tuning , RAG (Retrieval-Augmented Generation) , and multi-modal learning . Familiarity with agent orchestration frameworks and LLM tool chaining . Strong problem-solving and communication skills. Preferred Qualifications: Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools (MLflow, Weights & Biases). Knowledge of Reinforcement Learning or Meta-learning for agent training. Experience contributing to open-source projects or published papers in the field of AI.

About the Role We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems. Responsibilities Generative AI Pipeline Development Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads. Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs. Build CI/CD pipelines with integrated prompt regression testing and version control. Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows. Monitor system performance using tools like Langfuse or Prometheus. Data and Document Ingestion Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data. Apply preprocessing pipelines for text, images, and code. Ensure data integrity, format consistency, and security across sources. AI Service Integration Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.). Build internal APIs for smooth backend-AI communication. Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets. Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy. Retrieval-Augmented Generation (RAG) Pipelines Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API). Design custom retrieval strategies for multi-modal or multi-source documents. Apply post-retrieval ranking using DPO or feedback-based techniques. Improve contextual relevance through re-ranking, chunk merging, and scoring logic. LLM Integration and Optimization Manage prompt engineering, model interaction, and tuning workflows. Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design. Optimize generation using temperature tuning, token limits, and speculative decoding. Integrate observability and cost-monitoring into LLM workflows. Backend Services Ownership Design and maintain scalable backend services supporting GenAI applications. Implement monitoring, logging, and performance tracing. Build RBAC (Role-Based Access Control) and multi-tenant personalization. Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production. Required Skills and Qualifications Education Bachelors or Masters in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience 5+ years of experience in AI/ML engineering with end-to-end pipeline development. Hands-on experience building and deploying LLM/RAG systems in production. Strong experience with public cloud platforms (AWS, Azure, or GCP). Technical Skills Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch. Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph. Experience with RESTful API development and version control using Git. Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval. Familiarity with Docker, Kubernetes, and scalable microservice design. Experience with observability tools like Prometheus, Grafana, or Langfuse. Generative AI Specific Skills Knowledge of LLMs, VAEs, Diffusion Models, GANs. Experience building structured + unstructured RAG pipelines. Prompt engineering with safety controls, schema enforcement, and hallucination mitigation. Experience with prompt testing, caching strategies, output filtering, and fallback logic. Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods. Soft Skills Strong analytical, problem-solving, and debugging skills. Excellent collaboration with cross-functional teams: product, QA, and DevOps. Ability to work in fast-paced, agile environments and deliver production-grade solutions. Clear communication and strong documentation practices. Preferred Qualifications Experience with OCR, document parsing, and layout-aware chunking. Hands-on with MLOps and LLMOps tools for Generative AI. Contributions to open-source GenAI or AI infrastructure projects. Knowledge of GenAI governance, ethical deployment, and usage controls. Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI. Experience and Shift Experience: 5+ years Shift Time: 2:30 PM to 11:30 PM IST Location: Remote- Bengaluru,Hyderabad,Delhi / NCR,Chennai,Pune,Kolkata,Ahmedabad,Mumbai