We are on the lookout for a highly skilled Python LLM Engineer to join our growing AI team. If you are passionate about cutting-edge AI technologies, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG), this role is for you! You will be working with advanced AI tools such as LangChain, Pinecone, FAISS, Weaviate, and AWS Bedrock to build scalable AI-driven applications.
As an integral part of our AI division, you will be responsible for developing AI solutions that leverage multimodal AI, vector databases, agentic AI, and voice AI to drive innovation. You will also get the opportunity to work with top-tier LLM providers like OpenAI, Anthropic, Google Gemini, and Mistral.
Experience. 3+ years
Key Responsibilities
- Develop and optimize AI applications leveraging LLMs using LangChain and other frameworks.
- Build Retrieval-Augmented Generation (RAG) based AI systems to enhance information retrieval.
- Integrate and optimize LLM APIs such as OpenAI, Anthropic, Cohere, Google Gemini, and Mistral.
- Architect scalable AI solutions using AWS services, including AWS Bedrock.
- Develop vector search solutions using Pinecone, Weaviate, FAISS, or ChromaDB.
- Implement and optimize voice AI technologies, including Automatic Speech Recognition (ASR) and Text-to-Speech (TTS).
- Develop agentic AI systems that enable autonomous decision-making and multi-agent coordination.
- Optimize LLM inference for performance and cost efficiency using Python frameworks.
- Ensure security, scalability, and reliability of AI systems deployed in cloud environments.
- Design and implement end-to-end MLOps pipelines for training, deploying, and monitoring AI models.
- Work with multimodal AI models (text, image, audio, and video) using Python libraries like OpenCV, PIL, and SpeechRecognition.
- Stay up-to-date with AI advancements and continuously improve AI-driven solutions.
Required Skills & Experience
- Strong expertise in Large Language Models (LLMs) and their APIs (OpenAI, Anthropic, Cohere, Google Gemini, Mistral, etc.).
- Hands-on experience with LangChain and developing modular AI pipelines.
- Deep knowledge of Retrieval-Augmented Generation (RAG) and its implementation.
- Experience with multimodal AI, including processing text, images, audio, and video.
- Proficiency in Python for AI/ML development, with expertise in libraries like TensorFlow, PyTorch, Hugging Face, FastAPI, and LangChain.
- Strong background in vector databases (Pinecone, Weaviate, FAISS, ChromaDB).
- Familiarity with voice AI technologies and frameworks for ASR and TTS.
- Hands-on experience with AWS cloud services (AWS Bedrock, Lambda, S3, API Gateway, etc.).
- Understanding of CI/CD pipelines for AI model deployment and monitoring using tools like DVC, MLflow, and Airflow.
- Strong problem-solving skills and ability to work in a fast-paced AI-driven environment.
Preferred Qualifications
- Experience in reinforcement learning and self-improving AI agents.
- Expertise in prompt engineering, chain-of-thought prompting, and function calling.
- Prior experience in enterprise-grade AI application development.
- Exposure to AI security, compliance, and ethical AI considerations.
Why Join Platformance?
- Work on cutting-edge AI technologies and be at the forefront of AI innovation.
- Competitive salary, benefits, and stock options.
- Be part of a highly technical and dynamic AI-driven team.
- Opportunity to shape the future of AI applications and agentic AI systems.
- Remote work with flexible timings and a strong work-life balance.
Interview Process
- HR Screening. Culture fit assessment.
- Initial Technical Discussion. Live technical interview.
- Technical Assignment. A hands-on coding test to be completed within 4 days.
- Final Interview (Optional). Review of coding test and further discussion (if required).
How to Apply?
- Click on Apply and register/login on our portal.
- Complete the screening form and upload your updated resume.
- Get shortlisted and meet the client for the interview!
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