5 Mixtral Jobs

You will be working as an AI Platform Engineer in Bangalore as part of the GenAI COE Team. Your key responsibilities will involve developing and promoting scalable AI platforms for customer-facing applications. It will be essential to evangelize the platform with customers and internal stakeholders, ensuring scalability, reliability, and performance to meet business needs. Your role will also entail designing machine learning pipelines for experiment management, model management, feature management, and model retraining. Implementing A/B testing of models and designing APIs for model inferencing at scale will be crucial. You should have proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI. As an AI Platform Engineer, you will serve as a subject matter expert in LLM serving paradigms, with in-depth knowledge of GPU architectures. Expertise in distributed training and serving of large language models, along with proficiency in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM, will be required. Demonstrating proven expertise in model fine-tuning and optimization techniques to achieve better latencies and accuracies in model results will be part of your responsibilities. Reducing training and resource requirements for fine-tuning LLM and LVM models will also be essential. Having extensive knowledge of different LLM models and providing insights on their applicability based on use cases is crucial. You should have proven experience in delivering end-to-end solutions from engineering to production for specific customer use cases. Your proficiency in DevOps and LLMOps practices, along with knowledge of Kubernetes, Docker, and container orchestration, will be necessary. A deep understanding of LLM orchestration frameworks such as Flowise, Langflow, and Langgraph is also required. In terms of skills, you should be familiar with LLM models like Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, and Llama, as well as LLM Ops tools like ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, and Azure AI. Additionally, knowledge of databases/data warehouse systems like DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, and Google BigQuery, as well as cloud platforms such as AWS, Azure, and GCP, is essential. Proficiency in DevOps tools like Kubernetes, Docker, FluentD, Kibana, Grafana, and Prometheus, along with cloud certifications like AWS Professional Solution Architect and Azure Solutions Architect Expert, will be beneficial. Strong programming skills in Python, SQL, and Javascript are required for this full-time role, with an in-person work location.,

Responsibilities: * Design, develop & implement AI solutions using ML algorithms & NLP techniques. * Optimize performance through continuous learning & improvement. Annual bonus

Key Responsibilities: Fine-tune and deploy LLMs (e.g., GPT, LLaMA, Mistral) using frameworks like Hugging Face Transformers and LangChain. Build and optimize RAG pipelines with vector databases (e.g., Pinecone, FAISS, Weaviate). Engineer prompts for structured and reliable outputs across diverse use cases such as chatbots, summarization tools, and coding assistants. Implement scalable inference pipelines; optimize for latency, throughput, and cost using quantization, distillation, and other model optimization techniques. Collaborate with product, design, and engineering teams to integrate generative AI capabilities into user-facing features. Monitor and improve model performance, accuracy, safety, and compliance in production. Ensure responsible AI practices through content filtering, output sanitization, and ethical deployment. Required Skills: Proficiency in Python and familiarity with modern machine learning tools and libraries. Hands-on experience with LLM development using Hugging Face Transformers, LangChain, or LlamaIndex. Experience building and deploying RAG pipelines, including managing embeddings and vector search. Strong understanding of transformer architectures, tokenization, and prompt engineering techniques. Comfortable working with LLM APIs (e.g., OpenAI, Anthropic, Cohere) and serving models with FastAPI, Flask, or similar frameworks. Familiarity with deploying ML systems using Docker, Kubernetes, and cloud services (AWS, GCP, Azure). Experience with model evaluation, logging, and inference pipeline troubleshooting. Nice to Have: Exposure to multimodal models (e.g., text-to-image, video generation, TTS). Experience with reinforcement learning from human feedback (RLHF) or alignment techniques. Familiarity with open-source LLMs (e.g., Mistral, Mixtral, LLaMA, Falcon) and optimization tools (LoRA, quantization, PEFT). Knowledge of LangChain agents, tool integration, and memory management. Contributions to open-source GenAI projects, public demos, or blogs in the generative AI space. Basic proficiency in frontend development (e.g., React, Next.js) for rapid prototyping.

Key Responsibilities: Fine-tune and deploy LLMs (e.g., GPT, LLaMA, Mistral) using frameworks like Hugging Face Transformers and LangChain. Build and optimize RAG pipelines with vector databases (e.g., Pinecone, FAISS, Weaviate). Engineer prompts for structured and reliable outputs across diverse use cases such as chatbots, summarization tools, and coding assistants. Implement scalable inference pipelines; optimize for latency, throughput, and cost using quantization, distillation, and other model optimization techniques. Collaborate with product, design, and engineering teams to integrate generative AI capabilities into user-facing features. Monitor and improve model performance, accuracy, safety, and compliance in production. Ensure responsible AI practices through content filtering, output sanitization, and ethical deployment. Required Skills: Proficiency in Python and familiarity with modern machine learning tools and libraries. Hands-on experience with LLM development using Hugging Face Transformers, LangChain, or LlamaIndex. Experience building and deploying RAG pipelines, including managing embeddings and vector search. Strong understanding of transformer architectures, tokenization, and prompt engineering techniques. Comfortable working with LLM APIs (e.g., OpenAI, Anthropic, Cohere) and serving models with FastAPI, Flask, or similar frameworks. Familiarity with deploying ML systems using Docker, Kubernetes, and cloud services (AWS, GCP, Azure). Experience with model evaluation, logging, and inference pipeline troubleshooting. Nice to Have: Exposure to multimodal models (e.g., text-to-image, video generation, TTS). Experience with reinforcement learning from human feedback (RLHF) or alignment techniques. Familiarity with open-source LLMs (e.g., Mistral, Mixtral, LLaMA, Falcon) and optimization tools (LoRA, quantization, PEFT). Knowledge of LangChain agents, tool integration, and memory management. Contributions to open-source GenAI projects, public demos, or blogs in the generative AI space. Basic proficiency in frontend development (e.g., React, Next.js) for rapid prototyping.

Location: Hyderabad, India | Employment Type: Full-Time Experience Level: 2+Years Company: Covasant Contact Person: Ranjith Reddy 9703455109 | ranjith.palle@covasant.cm | linkedin.com/in/ranjith-r-75a766227 Build the Future of AI with Covasant At Covasant , we don't just work with AI we engineer the next era of it. We're hiring mid-level to senior developers and AI leads to help us build next-generation agentic AI systems that are intelligent, collaborative, and scalable. This is your chance to go beyond prompt engineering and shape the architecture of autonomous, multi-agent AI solutions using tools like LangGraph, AutoGen, CrewAI , and more. If youve got the skills and curiosity to work on what the AI world will be talking about next year , we want to hear from you. Your Role Design and develop multi-agent LLM systems using LangGraph, AutoGen, or CrewAI. Build and deploy MCP servers , LLM gateways , and design Agent-to-Agent collaboration flows. Fine-tune language models for verticals like healthcare, manufacturing, or finance. Architect retrieval-augmented generation (RAG) systems with vector stores like FAISS, Pinecone, or Weaviate. Integrate tools like LangSmith , GuardrailsAI , and knowledge graphs to ensure trust, safety, and observability. Collaborate cross-functionally with product, data science, and engineering teams. What You Bring 2+ years in software development, with strong Python skills. Proven expertise in one or more: LangGraph , AutoGen , CrewAI . Deep understanding of Agent-based AI , LLM orchestration , and RAG pipelines . Experience fine-tuning LLMs and applying prompt engineering and domain adaptation . Familiarity with tools like LangSmith , PromptGuard , or Guardrails frameworks. Bonus If You Have Cloud experience (AWS, Azure, GCP) Familiarity with Docker, Kubernetes Exposure to multi-modal models (LLaMA, Mistral, Falcon) Frontend tech: React, Angular, or Vue CI/CD, MLOps, or LLMOps knowledge Important We’re currently hiring mid-level, senior, and lead professionals with hands-on experience in AI/ML projects . These openings are not for freshers or professionals with less than 2 years of experience — but we are planning something exciting for early-career AI talent soon! Why Join Covasant Work on real-world agentic AI systems ahead of industry trends Collaborative and innovation-first work culture Competitive pay, benefits & performance incentives Hybrid/flexible work setup A chance to lead and influence the next chapter in AI Let’s Connect If this excites you — whether or not you're actively job hunting — don’t miss the chance to explore this game-changing opportunity. Ranjith Reddy – 9703455109 ranjith.palle@covasant.cm Connect with me on LinkedIn – I’d love to stay in touch, even if this isn’t the right time. Apply now or just start a conversation. The future of AI doesn’t wait — and neither should you.