299 Hugging Face Jobs - Page 12

Hiring AI & Data Retrieval Engineer with expertise in NLQ, Text-to-SQL, LLMs, LangChain, pgVector, PostgreSQL, vector search, Python, AI libraries, Agentic AI & API integration. Exp with NLP, RAG, BI tools, live projects & LLM fine-tuning preferred.

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.

Seeking a highly skilled and motivated Senior Generative AI Developer with approximately 2 years of hands-on experience in developing and deploying AI models, particularly in the generative space (e.g., NLP, AL/ML, AWS, Python, RASA, MCP, Flutter). Required Candidate profile 2+ yrs of exp in AI/ML dev, with focus on gen models. Prog skills in Python & ML libraries/frameworks Langchain, spacy, NLTK, Hugging Face Exp in AI bot/agent platforms like Make, POE, RetellAI

In this role you will: Develop and fine-tune LLMs for contract analysis, regulatory classification, and risk assessment. Implement Retrieval-Augmented Generation (RAG) using vector embeddings and hybrid DB-based querying to power DPIA and compliance workflows. Build AI-driven contract analysis systems to detect dark patterns, classify clauses, and provide remediation suggestions. Develop knowledge graph-based purpose taxonomies for privacy policies and PII classification. Automate data discovery for structured and unstructured data, classifying it into PII categories. Optimize sliding window chunking, token-efficient parsing, and context-aware summarization for legal and compliance texts. Build APIs and ML services for deploying models in a high-availability production environment. Collaborate with privacy, legal, and compliance teams to build AI solutions that power Privy s data governance tools. Stay ahead of the curve with agentic RAG, multi-modal LLMs, and self-improving models in the compliance domain. Skills Required: LLM , RAG , AgenticAI , NLP , Python , Problem solving Candidate Attributes: Must-Have Skills 3-5 years of experience in Machine Learning, NLP, and LLM-based solutions. Strong expertise in fine-tuning and deploying LLMs (GPT-4, Llama, Mistral, or custom models). Experience with RAG-based architectures, including vector embeddings (FAISS, ChromaDB, Weaviate, Pinecone, or similar). Hands-on with agentic RAG, sliding window chunking, and efficient context retrieval techniques. Deep understanding of privacy AI use cases, including contract analysis, regulatory classification, and PII mapping. Proficiency in Python and frameworks like PyTorch, TensorFlow, JAX, Hugging Face, or LangChain. Experience in building scalable AI APIs and microservices. Exposure to MLOps practices, including model monitoring, inference optimization, and API scalability. Experience working with at least one cloud provider (AWS, GCP, or Azure). Good-to-Have Skills Experience in hybrid AI architectures combining vector search + relational databases. Familiarity with functional programming languages (Go, Elixir, Rust, etc.). Understanding of privacy compliance frameworks (DPDP Act, GDPR, CCPA, ISO 27701). Exposure to Kubernetes, Docker, and ML deployment best practices. Contributions to open-source LLM projects or privacy AI research.

Hello, Urgent job openings for Data Science(Gen AI & LLM) :- Team Lead/ Sr Engineer @ GlobalData(Hyd). Looking for candidates with experience in Gen AI, LLM, RAG frameworks Job Description given below please go through to understand the requirement. if requirement is matching to your profile share your updated resume @ mail id (m.salim@globaldata.com). Mention Subject Line :- Applying for Data Science(Gen AI/LLM) - Lead/SSE @ GlobalData(Hyd) Share your details in the mail :- Full Name : Mobile # : Qualification : Company Name : Designation : Total Work Experience Years : Current CTC : Expected CTC : Notice Period : Current Location/willing to relocate to Hyd? : Office Address : 3rd Floor, Jyoti Pinnacle Building, Opp to Prestige IVY League Appt, Kondapur Road, Hyderabad, Telangana-500081. Job Description: Senior Engineer/Lead - Gen AI/LLM Position Overview:- We are seeking a talented Lead/Senior Engineer AI/LLM Developer to join our team and drive the development of cutting-edge artificial intelligence solutions using large language models. In this role, you will design, build, and optimize LLM-based applications that solve complex business problems while maintaining high standards of performance, scalability, and reliability. Key Responsibilities:- Design and implement sophisticated AI applications leveraging state-of-the-art LLM technologies Develop efficient solutions for LLM integration, fine-tuning, and deployment Optimize model performance, latency, and resource utilization Build and maintain robust data pipelines for training and inference Implement advanced prompt engineering techniques and retrieval-augmented generation (RAG) Develop evaluation frameworks to measure AI system performance and output quality Collaborate with cross-functional teams to understand requirements and deliver solutions Mentor junior developers and share AI/LLM knowledge across the organization Participate in code reviews and ensure adherence to best practices Role Requirement :- 5+ years of software development experience with at least 3 years focused on AI/ML technologies Strong experience working with transformer-based models and LLM APIs Proficiency in Python and relevant AI/ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with vector databases and semantic search technologies Solid understanding of prompt engineering, RAG, and fine-tuning techniques Familiarity with cloud platforms (AWS, Azure, GCP) for AI model deployment Strong problem-solving skills and attention to detail.' Experience with LLM optimization techniques like quantization and distillation Knowledge of AI evaluation metrics and benchmarking methodologies Understanding of multimodal AI systems (text, image, audio) Experience with containerization and orchestration tools (Docker, Kubernetes) Contributions to open-source AI projects or research publications Familiarity with AI ethics and responsible AI development Qualifications:- Bachelors degree in computer science, AI, Machine Learning, or related field Master's in AI, B,Tech,MCA, or related field Thanks & Regards, Salim (Human Resources)

Looking for a highly motivated, self-driven AI Experienced Data Scientist for building differentiating solutions and for implementing AI and Generative AI (Gen AI) based solutions for customer's business problems. As an AI Solution Engineer , build AI and Gen AI empowered practical in-depth solutions for solving customers business problems. As an AI and Data Solution Engineer , design and implement Cloud Data, AI, and Gen AI based solutions , including LLM-powered systems, for customer programs. Apply statistics, modeling, LLMs , and machine learning to improve the efficiency of systems and relevance algorithms across our business application products. Requirements Bachelors degree or Masters degree in Computer Science / AIML / Data Science. 4 to 6 years of overall experience and hands-on experience with the design and implementation of Machine Learning models, Deep Learning models, and Gen AI models (e.g., GPT, LLaMA, Mistral) for solving business problems. Proven experience working with Generative AI technologies , including prompt engineering, fine-tuning large language models (LLMs), embeddings, vector databases (e.g., FAISS, Pinecone), and Retrieval-Augmented Generation (RAG) systems. Expertise in R, Python (NumPy, Scikit-learn, Pandas), TensorFlow, PyTorch, transformers (e.g., Hugging Face) , or MLlib. Expertise in cloud-based data and AI solution design and implementation using GCP / AWS / Azure , including the use of their Gen AI services. Good experience in building complex and scalable ML and Gen AI solutions and deploying them into production environments. Experience with scripting in SQL, extracting large datasets, and designing ETL flows. Excellent problem-solving and analytical skills with the ability to translate business requirements into data science and Gen AI solutions. Effective communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

We are seeking an experienced and innovative AI Specialist to join our dynamic team. The ideal candidate will possess deep expertise in artificial intelligence, machine learning, and neural networks, with a proven track record of designing, developing, and deploying AI solutions. The candidate should be adept at analyzing large datasets, developing optimized algorithms, and integrating AI models into production environments. This role offers the opportunity to contribute to cutting-edge AI applications that solve real-world problems and drive business value Key Responsibilities: AI System Design & Development: Design, develop, and implement AI systems and applications, including machine learning models, neural networks, and natural language processing (NLP) algorithms. Create and optimize AI algorithms for tasks such as image recognition, speech processing, and predictive analytics. Data Analysis & Feature Engineering: Analyze large datasets to identify trends, patterns, and actionable insights. Perform data preprocessing, data wrangling, and feature engineering to enhance model performance. Model Training & Optimization: Train and fine-tune machine learning models using labeled and unlabeled data to improve model accuracy and efficiency. Continuously monitor and optimize models for better performance. AI Model Integration: Deploy machine learning models into production environments, ensuring scalability and reliability. Integrate AI technologies with existing software and business processes using REST APIs. Utilize Docker and Kubernetes to containerize and manage AI models. Research & Innovation: Stay up to date with the latest advancements in AI and machine learning technologies. Explore and experiment with new AI applications, tools, and methodologies to enhance system capabilities. Collaboration & Agile Development: Work closely with cross-functional teams in an Agile environment to develop AI-driven solutions that meet business requirements. Communicate findings and results to stakeholders, ensuring AI solutions align with business goals. Primary Skills and Qualifications: AI and Machine Learning Expertise: Strong knowledge of supervised and unsupervised learning techniques, including classification, regression, clustering, and reinforcement learning. Hands-on experience developing and deploying machine learning models in production environments. Technical Proficiency: Programming Languages: Advanced proficiency in Python, with additional experience in R and SQL as a plus. ML Libraries/Frameworks: Expertise in TensorFlow, PyTorch, and scikit-learn. Cloud Platforms: Hands-on experience with AWS, Azure, or Google Cloud for model deployment and management. Model Deployment & Integration: Familiarity with REST APIs, Docker, and Kubernetes for seamless model integration. Secondary Skills and Preferred Qualifications: Natural Language Processing (NLP): Experience with NLP frameworks such as NLTK, spaCy, Hugging Face, and BERT. Agile Methodologies: Knowledge of Agile practices, with experience using tools like JIRA for project management. Mathematics and Statistics: Strong foundation in probability, statistics, and linear algebra to support model development and evaluation. Data Handling & Preprocessing: Proficiency in data cleaning, transformation, and feature engineering to prepare data for modeling.

Develop and deploy AI-based applications leveraging LLMs and Generative AI models like GPT, Gemini, or similar frameworks Build scalable backend systems using Python, ensuring seamless integration with UI/UX components Design and optimize generative AI models to address diverse business challenges Fine-tune pre-trained LLMs to align with specific use cases Work closely with cross-functional teams, including data scientists, UI/UX developers, and product managers, to deliver robust solutions Collaborate with clients to gather requirements and develop customized AI capabilities Deploy solutions on cloud platforms (Azure, AWS, or GCP) and ensure system scalability and performance

Overview We are seeking an experienced Reporting GenAI Consultant with a strong background in developing AI-driven reporting solutions. This role focuses on building and integrating Generative AI capabilities into BI platforms to enable natural language insights, automated report generation, and interactive dialogue with data. The ideal candidate will have hands-on experience working with LLMs, prompt engineering, and modern data visualization tools. Responsibilities Design, develop, and deploy GenAI-based reporting solutions that generate insights summaries, dashboards, and narrative analytics using structured and unstructured data. Build natural language interfaces and conversational agents for querying data (Dialogue with Data), enabling users to interact with reports through plain English. Integrate GenAI features (like ChatGPT, Azure OpenAI, or Vertex AI) with enterprise BI platforms (Power BI, Tableau, Qlik, ThoughtSpot, etc.). Implement automated insight generation using LLMs to summarize trends, detect anomalies, and generate key takeaways. Collaborate with data engineering and BI teams to optimize data models and ensure clean, prompt-ready datasets. Design and fine-tune prompts and templates for contextual report summarization and storytelling . Conduct POCs and pilots to evaluate the feasibility and impact of GenAI-driven reporting use cases. Ensure solutions are secure, scalable, and compliant with enterprise governance policies. Qualifications 10+ years of experience in Business Intelligence / Analytics with 12 years in Generative AI implementations . Strong experience in Power BI with exposure to augmented analytics features. Experience working with LLMs (OpenAI, Azure OpenAI, Hugging Face, Google PaLM, etc.) for natural language understanding and summarization. Expertise in prompt engineering , few-shot learning, and custom summarization models. Good understanding of data storytelling , narrative generation , and auto-generated insights . Experience in integrating APIs for AI models into web or reporting tools. Familiarity with Python or JavaScript for model integration and backend logic. Excellent communication and stakeholder management skills. Preferred Qualifications: Experience with RAG (Retrieval-Augmented Generation) , LangChain , or similar frameworks. Exposure to voice-based analytics or speech-to-insight solutions. Knowledge of data governance, privacy (GDPR/CPRA) , and enterprise security standards . Familiarity with cloud platforms : Azure.

Senior Engineer (GenAI & Prompt Engineering) | Xebia We are looking for a highly experienced GenAI Engineer with deep expertise in Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Search Systems to integrate GenAI into our engineering & DevOps ecosystem. This is a contractor role where you will design intelligent assistants & agents to augment CI/CD workflows, knowledge retrieval, incident handling, and developer productivity using LLMs, Python, and NLP pipelines . Key Responsibilities Design & optimize prompts for LLM workflows ensuring accuracy & relevance Build RAG pipelines using vector DBs (FAISS, Pinecone, Weaviate, Qdrant) Integrate LLMs into internal tools, CI/CD & observability dashboards Implement GenAI solutions using LangChain, LlamaIndex, Haystack, Hugging Face etc. Fine-tune open-source/commercial LLMs (OpenAI, Claude, Cohere, Mistral) for domain use cases Collaborate with DevOps & platform teams to drive automation with GenAI Ensure data privacy, governance & ethical AI practices Required Skills 6-10+ years in engineering, with 2+ years hands-on in GenAI/LLMs/NLP Strong Python skills with experience in LangChain, Hugging Face, LlamaIndex Deep knowledge of vector databases & embeddings Proven experience designing & deploying RAG architectures Experience integrating LLM APIs (OpenAI, Azure OpenAI, Claude, etc.) Nice to Have Experience with multi-modal models / fine-tuning LLMs Familiarity with developer-facing GenAI use cases : infra-as-code review, changelog generation, log triage Exposure to Kubernetes, GitOps, DevSecOps Work Location & Mode Any Xebia Location : Chennai, Bangalore, Hyderabad, Pune, Gurugram, Bhopal, Jaipur Hybrid model 3 days/week from office Important Only Immediate Joiners or max 2 weeks notice period will be considered How to Apply Send your profile to vijay.s@xebia.com with the subject line: Application Senior Engineer (GenAI & Prompt Engineering) Please share the following details along with your CV: Full Name Total Experience Current CTC Expected CTC Current Location Preferred Xebia Location (from above) Notice Period / Last Working Day (if serving) Primary Skills LinkedIn Profile

Position : Machine Learning Engineer - Generative AI / LLMs (Onsite - Bengaluru) Experience : 3+ years of industry experience in ML, software engineering, and data engineering. Education : Masters degree or equivalent experience in Machine Learning. Location : Bangalore (HSR Layout) Job Description: We are looking for a talented Machine Learning Engineer to join our AI team at our Bengaluru office (Onsite Only). In this role, you will work on cutting-edge solutions leveraging Generative AI, Large Language Models (LLMs), NLP, and Computer Vision to solve complex real-world problems. Responsibilities: Design, build, and deploy ML models for NLP, Computer Vision, LLMs, and Generative AI use cases. Develop and maintain robust, scalable ML pipelines for training, evaluation, and deployment. Optimize model performance using cloud-based GPU resources and best practices. Implement scalable inference systems, A/B testing, and monitor production models. Collaborate with cross-functional teams to deliver AI-driven product features. Follow best practices in MLOps, LLMOps, Kubernetes, and Docker. Required Skills: Strong knowledge of Machine Learning, Deep Learning, NLP, and LLMs. Experience with Python, PyTorch, TensorFlow. Familiarity with Generative AI frameworks: Hugging Face, LangChain, MLFlow, LangGraph, LangFlow. Cloud platforms: AWS (SageMaker, Bedrock), Azure AI. Databases: MongoDB, PostgreSQL, Pinecone, ChromaDB. MLOps tools, Kubernetes, Docker. Preferred Qualifications: 3+ years of experience in ML/AI product development. Proven experience building and deploying NLP, LLM, and Generative AI solutions. Experience working with LLMOps best practices. Strong programming skills in Python and familiarity with JavaScript. Interested candidates kindly share your CV and below details to usha.sundar@adecco.com 1) Present CTC (Fixed + VP) - 2) Expected CTC - 3) No. of years experience - 4) Notice Period - 5) Offer-in hand - 6) Reason of Change - 7) Present Location -

We are hiring an experienced AI Engineer / ML Specialist with deep expertise in Large Language Models (LLMs), who can fine-tune, customize, and integrate state-of-the-art models like OpenAI GPT, Claude, LLaMA, Mistral, and Gemini into real-world business applications. The ideal candidate should have hands-on experience with foundation model customization, prompt engineering, retrieval-augmented generation (RAG), and deployment of AI assistants using public cloud AI platforms like Azure OpenAI, Amazon Bedrock, Google Vertex AI, or Anthropics Claude. Key Responsibilities: LLM Customization & Fine-Tuning Fine-tune popular open-source LLMs (e.g., LLaMA, Mistral, Falcon, Mixtral) using business/domain-specific data. Customize foundation models via instruction tuning, parameter-efficient fine-tuning (LoRA, QLoRA, PEFT), or prompt tuning. Evaluate and optimize the performance, factual accuracy, and tone of LLM responses. AI Assistant Development Build and integrate AI assistants/chatbots for internal tools or customer-facing applications. Design and implement Retrieval-Augmented Generation (RAG) pipelines using tools like LangChain, LlamaIndex, Haystack, or OpenAI Assistants API. Use embedding models, vector databases (e.g., Pinecone, FAISS, Weaviate, ChromaDB), and cloud AI services. Must have experience of finetuning, and maintaining microservices or LLM driven databases. Cloud Integration Deploy and manage LLM-based solutions on AWS Bedrock, Azure OpenAI, Google Vertex AI, Anthropic Claude, or OpenAI API. Optimize API usage, performance, latency, and cost. Secure integrations with identity/auth systems (OAuth2, API keys) and logging/monitoring. Evaluation, Guardrails & Compliance Implement guardrails, content moderation, and RLHF techniques to ensure safe and useful outputs. Benchmark models using human evaluation and standard metrics (e.g., BLEU, ROUGE, perplexity). Ensure compliance with privacy, IP, and data governance requirements. Collaboration & Documentation Work closely with product, engineering, and data teams to scope and build AI-based solutions. Document custom model behaviors, API usage patterns, prompts, and datasets. Stay up-to-date with the latest LLM research and tooling advancements. Required Skills & Qualifications: Bachelors or Masters in Computer Science, AI/ML, Data Science, or related fields. 36+ years of experience in AI/ML, with a focus on LLMs, NLP, and GenAI systems. Strong Python programming skills and experience with Hugging Face Transformers, LangChain, LlamaIndex. Hands-on with LLM APIs from OpenAI, Azure, AWS Bedrock, Google Vertex AI, Claude, Cohere, etc. Knowledge of PEFT techniques like LoRA, QLoRA, Prompt Tuning, Adapters. Familiarity with vector databases and document embedding pipelines. Experience deploying LLM-based apps using FastAPI, Flask, Docker, and cloud services. Preferred Skills: Experience with open-source LLMs: Mistral, LLaMA, GPT-J, Falcon, Vicuna, etc. Knowledge of AutoGPT, CrewAI, Agentic workflows, or multi-agent LLM orchestration. Experience with multi-turn conversation modeling, dialogue state tracking. Understanding of model quantization, distillation, or fine-tuning in low-resource environments. Familiarity with ethical AI practices, hallucination mitigation, and user alignment. Tools & Technologies: Category Tools & Platforms LLM Frameworks Hugging Face, Transformers, PEFT, LangChain, LlamaIndex, Haystack LLMs & APIs OpenAI (GPT-4, GPT-3.5), Claude, Mistral, LLaMA, Cohere, Gemini, Azure OpenAI Vector Databases FAISS, Pinecone, Weaviate, ChromaDB Serving & DevOps Docker, FastAPI, Flask, GitHub Actions, Kubernetes Deployment Platforms AWS Bedrock, Azure ML, GCP Vertex AI, Lambda, Streamlit Monitoring Prometheus, MLflow, Langfuse, Weights & Biases.

Position Overview : We are seeking an experienced NLP & LLM Specialist to join our team. The ideal candidate will have deep expertise in working with transformer-based models, including GPT, BERT, T5, RoBERTa, and similar models. This role requires experience in fine-tuning these pre-trained models on domain-specific tasks, as well as crafting and optimizing prompts for natural language processing tasks such as text generation, summarization, question answering, classification, and translation. The candidate should be proficient in Python and familiar with NLP libraries like Hugging Face, SpaCy, and NLTK, with a solid understanding of model evaluation metrics. Roles and Responsibilities : - Model Expertise : Work with transformer models such as GPT, BERT, T5, RoBERTa, and others for a variety of NLP tasks, including text generation, summarization, classification, and translation. - Model Fine-Tuning : Fine-tune pre-trained models on domain-specific datasets to improve performance for specific applications such as summarization, text generation, and question answering. - Prompt Engineering : Craft clear, concise, and contextually relevant prompts to guide transformer-based models towards generating desired outputs for specific tasks. - Iterate on prompts to optimize model performance. - Instruction-Based Prompting : Implement instruction-based prompting to guide the model toward achieving specific goals, ensuring that the outputs are contextually accurate and aligned with task objectives. - Zero-shot, Few-shot, Many-shot Learning : Utilize zero-shot, few-shot, and many-shot learning techniques to improve model performance without the need for full retraining. - Chain-of-Thought (CoT) Prompting : Implement Chain-of-Thought (CoT) prompting to guide models through complex reasoning tasks, ensuring that the outputs are logically structured and provide step-by-step explanations. - Model Evaluation : Use evaluation metrics such as BLEU, ROUGE, and other relevant metrics to assess and improve the performance of models for various NLP tasks. - Model Deployment : Support the deployment of trained models into production environments and integrate them into existing systems for real-time applications. - Bias Awareness : Be aware of and mitigate issues related to bias, hallucinations, and knowledge cutoffs in LLMs, ensuring high-quality and reliable outputs. - Collaboration : Collaborate with cross-functional teams including engineers, data scientists, and product managers to deliver efficient and scalable NLP solutions. Must Have Skill : - Overall 7 years with at least 5+ years of experience working with transformer-based models and NLP tasks, with a focus on text generation, summarization, question answering, classification, and similar tasks. - Expertise in transformer models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), RoBERTa, and similar models. - Familiarity with model architectures, attention mechanisms, and self-attention layers that enable LLMs to generate human-like text. - Experience in fine-tuning pre-trained models on domain-specific datasets for tasks such as text generation, summarization, question answering, classification, and translation. - Familiarity with concepts like attention mechanisms, context windows, tokenization, and embedding layers. - Awareness of biases, hallucinations, and knowledge cutoffs that can affect LLM performance and output quality. - Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs. - Experience in instruction-based prompting. - Use of zero-shot, few-shot, and many-shot learning techniques for maximizing model performance without retraining. - Experience in iterating on prompts to refine outputs, test model performance, and ensure consistent results. - Crafting prompt templates for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs. - Expertise in chain-of-thought (CoT) prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. - Proficiency in Python and experience with NLP libraries (e.g., Hugging Face, SpaCy, NLTK). - Experience with transformer-based models (e.g., GPT, BERT, T5) for text generation tasks. - Experience in training, fine-tuning, and deploying machine learning models in an NLP context. - Understanding of model evaluation metrics (e.g., BLEU, ROUGE). Qualification : - BE/B.Tech or Equivalent degree in Computer Science or related field. - Excellent communication skills in English, both verbal and written.

We are hiring an experienced AI Engineer / ML Specialist with deep expertise in Large Language Models (LLMs), who can fine-tune, customize, and integrate state-of-the-art models like OpenAI GPT, Claude, LLaMA, Mistral, and Gemini into real-world business applications. The ideal candidate should have hands-on experience with foundation model customization, prompt engineering, retrieval-augmented generation (RAG), and deployment of AI assistants using public cloud AI platforms like Azure OpenAI, Amazon Bedrock, Google Vertex AI, or Anthropics Claude. Key Responsibilities: LLM Customization & Fine-Tuning Fine-tune popular open-source LLMs (e.g., LLaMA, Mistral, Falcon, Mixtral) using business/domain-specific data. Customize foundation models via instruction tuning, parameter-efficient fine-tuning (LoRA, QLoRA, PEFT), or prompt tuning. Evaluate and optimize the performance, factual accuracy, and tone of LLM responses. AI Assistant Development Build and integrate AI assistants/chatbots for internal tools or customer-facing applications. Design and implement Retrieval-Augmented Generation (RAG) pipelines using tools like LangChain, LlamaIndex, Haystack, or OpenAI Assistants API. Use embedding models, vector databases (e.g., Pinecone, FAISS, Weaviate, ChromaDB), and cloud AI services. Must have experience of finetuning, and maintaining microservices or LLM driven databases. Cloud Integration Deploy and manage LLM-based solutions on AWS Bedrock, Azure OpenAI, Google Vertex AI, Anthropic Claude, or OpenAI API. Optimize API usage, performance, latency, and cost. Secure integrations with identity/auth systems (OAuth2, API keys) and logging/monitoring. Evaluation, Guardrails & Compliance Implement guardrails, content moderation, and RLHF techniques to ensure safe and useful outputs. Benchmark models using human evaluation and standard metrics (e.g., BLEU, ROUGE, perplexity). Ensure compliance with privacy, IP, and data governance requirements. Collaboration & Documentation Work closely with product, engineering, and data teams to scope and build AI-based solutions. Document custom model behaviors, API usage patterns, prompts, and datasets. Stay up-to-date with the latest LLM research and tooling advancements. Required Skills & Qualifications: Bachelors or Masters in Computer Science, AI/ML, Data Science, or related fields. 36+ years of experience in AI/ML, with a focus on LLMs, NLP, and GenAI systems. Strong Python programming skills and experience with Hugging Face Transformers, LangChain, LlamaIndex. Hands-on with LLM APIs from OpenAI, Azure, AWS Bedrock, Google Vertex AI, Claude, Cohere, etc. Knowledge of PEFT techniques like LoRA, QLoRA, Prompt Tuning, Adapters. Familiarity with vector databases and document embedding pipelines. Experience deploying LLM-based apps using FastAPI, Flask, Docker, and cloud services. Preferred Skills: Experience with open-source LLMs: Mistral, LLaMA, GPT-J, Falcon, Vicuna, etc. Knowledge of AutoGPT, CrewAI, Agentic workflows, or multi-agent LLM orchestration. Experience with multi-turn conversation modeling, dialogue state tracking. Understanding of model quantization, distillation, or fine-tuning in low-resource environments. Familiarity with ethical AI practices, hallucination mitigation, and user alignment. Tools & Technologies: Category Tools & Platforms LLM Frameworks Hugging Face, Transformers, PEFT, LangChain, LlamaIndex, Haystack LLMs & APIs OpenAI (GPT-4, GPT-3.5), Claude, Mistral, LLaMA, Cohere, Gemini, Azure OpenAI Vector Databases FAISS, Pinecone, Weaviate, ChromaDB Serving & DevOps Docker, FastAPI, Flask, GitHub Actions, Kubernetes Deployment Platforms AWS Bedrock, Azure ML, GCP Vertex AI, Lambda, Streamlit Monitoring Prometheus, MLflow, Langfuse, Weights & Biases

Job Overview: We are looking for a highly skilled AI & Agentic Developer to design, build, and deploy intelligent autonomous systems. This role involves developing AI-powered agents that can perceive, reason, and act independently, leveraging LLMs, reinforcement learning, and multi-agent systems. You will work on cutting-edge AI frameworks like LangChain, AutoGen, CrewAI, and MLOps pipelines for scalable deployment. Key Responsibilities: AI & Machine Learning Development: Develop, train, and fine-tune machine learning (ML) and deep learning (DL) models. Implement LLMs, NLP, reinforcement learning (RL), and computer vision (CV) models. Optimize retrieval-augmented generation (RAG) systems with vector databases (FAISS, Pinecone, Chroma). Work on multimodal AI systems integrating text, image, and audio processing. Agentic AI Systems Development: Build autonomous AI agents that can make decisions, learn from feedback, and interact with environments. Implement multi-agent frameworks using LangChain, CrewAI, AutoGen, BabyAGI, MetaGPT. Integrate AI agents with APIs, databases, and enterprise applications for real-world deployment. Software Engineering & MLOps: Design and deploy AI models using Docker, Kubernetes, and CI/CD pipelines. Implement MLOps workflows for continuous training, monitoring, and model retraining. Optimize AI solutions for cloud platforms (AWS, Azure, GCP) and edge computing. AI Governance & Security: Implement AI safety measures, including adversarial defense and bias mitigation. Ensure compliance with AI regulations (GDPR, HIPAA, SOC 2) and ethical AI principles. Secure AI agents against prompt injection, model poisoning, and adversarial attacks. Required Skills & Qualifications: Technical Skills: Strong programming skills in Python, Java, or R. Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn). Experience with LLMs & NLP (Hugging Face, OpenAI API, BERT, GPT, Claude). Proficiency in multi-agent frameworks (LangChain, CrewAI, AutoGen). Knowledge of reinforcement learning (PPO, A3C, DDPG, SAC). Experience with vector databases (FAISS, Pinecone, Chroma) and knowledge graphs. Strong foundation in MLOps, cloud AI services (AWS, Azure AI, Google Vertex AI). Soft Skills: Strong analytical and problem-solving skills. Ability to work in a fast-paced, cross-functional team. Excellent communication and documentation skills.

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI , Reinforcement Learning , and Natural Language Processing (NLP) to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation and decision-making and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, retrieval-augmented generation (RAG), and multi-agent coordination. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere Vector Search & Memory: Pinecone, FAISS, ChromaDB, Weaviate Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform Preferred Qualifications: Experience with agent orchestration tools like LangGraph , ReAct, or Toolformer. Exposure to real-time or asynchronous multi-agent systems. Familiarity with prompt engineering, context management, and RAG pipelines. Contributions to open-source agent/LLM projects or research publications in AI/ML. Why Join Us? Be part of an AI-first product company solving real-world automation and decision-making challenges. Work with a team of AI innovators and contribute to cutting-edge research and applications.

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI , Reinforcement Learning , and Natural Language Processing (NLP) to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation and decision-making and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, retrieval-augmented generation (RAG), and multi-agent coordination. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere Vector Search & Memory: Pinecone, FAISS, ChromaDB, Weaviate Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform Preferred Qualifications: Experience with agent orchestration tools like LangGraph , ReAct, or Toolformer. Exposure to real-time or asynchronous multi-agent systems. Familiarity with prompt engineering, context management, and RAG pipelines. Contributions to open-source agent/LLM projects or research publications in AI/ML. Why Join Us? Be part of an AI-first product company solving real-world automation and decision-making challenges. Work with a team of AI innovators and contribute to cutting-edge research and applications.

Role & responsibilities Overall 7 years with at least 5+ years of experience working with transformer-based models and NLP tasks, with a focus on text generation, summarization, question answering, classification, and similar tasks. Expertise in transformer models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), RoBERTa, and similar models. Familiarity with model architectures, attention mechanisms, and self-attention layers that enable LLMs to generate human-like text. Experience in fine-tuning pre-trained models on domain-specific datasets for tasks such as text generation, summarization, question answering, classification, and translation. Familiarity with concepts like attention mechanisms, context windows, tokenization, and embedding layers. Awareness of biases, hallucinations, and knowledge cutoffs that can affect LLM performance and output quality. Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs. Experience in instruction-based prompting Use of zero-shot, few-shot, and many-shot learning techniques for maximizing model performance without retraining. Experience in iterating on prompts to refine outputs, test model performance, and ensure consistent results. Crafting prompt templates for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs. Expertise in chain-of-thought (CoT) prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. Proficiency in Python and experience with NLP libraries (e.g., Hugging Face, SpaCy, NLTK). Experience with transformer-based models (e.g., GPT, BERT, T5) for text generation tasks. Experience in training, fine-tuning, and deploying machine learning models in an NLP context. Understanding of model evaluation metrics (e.g., BLEU, ROUGE)

AI Software Engineer Location : Hyderabad - Hybrid Notice Period : 0 to 30 days Looking for Senior Fullstack AI Engineer with Python, AI/ML, GenAI, LLM, and RAG combined with frontend technologies, who can build and deploy complex, end-to-end AI applications. Core Responsibilities End-to-End AI Application Development: Design, develop, and deploy full-stack applications that integrate Generative AI and Large Language Models (LLMs). This includes building both the AI-powered backend services and the user-facing interfaces. Backend & AI Engineering: Use Python as the primary language to build robust APIs (RESTful, GraphQL) that serve AI models. Implement Retrieval-Augmented Generation (RAG) systems to enhance LLM performance and ground responses in specific, private data. Frontend Development: Create intuitive and responsive user interfaces using technologies like React, Angular, or Vue.js. This ensures that the user can effectively interact with and benefit from the powerful AI models running on the backend. System Architecture & Optimization: Architect scalable and high-performance solutions. The role involves designing the overall system, from data pipelines for AI models to cloud deployment strategies on platforms like AWS, Azure, or GCP. Mentorship and Leadership: As a senior engineer, you'll be expected to mentor junior team members, conduct code reviews, and contribute to the team's technical strategy and best practices. Key Skills and Qualifications Python: Extensive experience with Python and its AI/ML libraries, such as PyTorch, TensorFlow, and Hugging Face. Generative AI & LLMs: Hands-on experience with GenAI models, prompt engineering, fine-tuning, and integrating various LLMs (e.g., GPT-4, Llama, Gemini). RAG: Practical experience in building and optimizing RAG pipelines, including working with vector databases like Pinecone or ChromaDB. Frontend: Proficiency in JavaScript/TypeScript and a modern frontend framework like React. Full Stack: Experience in building full-stack applications and understanding of microservices architecture and cloud-native development. Please refer to the Company Profile: https://www.maantic.com/

Role & Responsibilities: Lead end-to-end development of AI-powered solutions across Computer Vision, NLP, and predictive modeling. Architect and implement scalable AI services and integrate them using REST APIs and microservices. Drive MLOps best practices and manage CI/CD pipelines using tools like MLflow, Kubeflow, and Docker. Fine-tune large language models and build NLP applications such as document intelligence, text summarization, semantic search, and QA systems. Mentor a team of engineers and collaborate with product managers to define the AI roadmap. Conduct regular code reviews and ensure the delivery of high-quality, production-ready AI systems. Preferred Candidate Profile: 5+ years of hands-on experience in AI/ML development with strong expertise in deep learning and transformer-based architectures. Proficient in Python and experienced with frameworks such as PyTorch, TensorFlow, and Hugging Face. Strong understanding of RESTful APIs, Docker, Kubernetes, and MLOps workflows. Prior experience in regulated domains such as Fintech or BFSI is a plus. Proven team leadership experience with excellent project management and communication skills. Perks and Benefits: Competitive salary and performance-driven growth opportunities. Work on impactful AI solutions in the Fintech/Regtech space. Collaborative work environment with a strong focus on innovation. Opportunity to work with leading financial institutions and regulatory bodies.

Job role - gen AI AWS Experience - 3 to 6 years Location - Bangalore Notice period - immediate joiner We are seeking a highly skilled and innovative Consultant with hands-on experience in Generative AI, Prompt Engineering, Data Science, Python programming, Machine Learning Algorithms and AWS Bedrock services. The ideal candidate will design, develop, and deploy intelligent solutions leveraging cutting-edge AI technologies and large language models (LLMs). Key Responsibilities: • Design and implement solutions using Generative AI models and AWS Bedrock services • Develop and optimize prompts for various use cases including chatbots, content generation, and automation • Integrate and configure LLMs for banks applications • Collaborate with cross-functional teams to integrate AI solutions into existing systems • Stay updated with the latest advancements in AI, LLMs, and cloud-based AI services Required Skills & Qualifications: • Proven experience with Generative AI frameworks and LLMs (e.g., OpenAI, Hugging Face, Amazon Bedrock) • Strong expertise in Prompt Engineering and optimization techniques • Solid foundation in Data Science including statistics, ML and data wrangling • Proficiency in Python and libraries such as NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch • Hands-on experience with AWS Bedrock services and LLM integration/configuration • Experience with API integration, model deployment, and cloud platforms • Excellent problem-solving and communication skills • Experience in writing clean, efficient, and scalable Python code for data processing, model training, and deployment • Experience in analyzing complex datasets to extract insights and build predictive models

Job Overview: We are looking for a highly skilled AI & Agentic Developer to design, build, and deploy intelligent autonomous systems. This role involves developing AI-powered agents that can perceive, reason, and act independently, leveraging LLMs, reinforcement learning, and multi-agent systems. You will work on cutting-edge AI frameworks like LangChain, AutoGen, CrewAI, and MLOps pipelines for scalable deployment. Key Responsibilities: AI & Machine Learning Development: Develop, train, and fine-tune machine learning (ML) and deep learning (DL) models. Implement LLMs, NLP, reinforcement learning (RL), and computer vision (CV) models. Optimize retrieval-augmented generation (RAG) systems with vector databases (FAISS, Pinecone, Chroma). Work on multimodal AI systems integrating text, image, and audio processing. Agentic AI Systems Development: Build autonomous AI agents that can make decisions, learn from feedback, and interact with environments. Implement multi-agent frameworks using LangChain, CrewAI, AutoGen, BabyAGI, MetaGPT. Integrate AI agents with APIs, databases, and enterprise applications for real-world deployment. Software Engineering & MLOps: Design and deploy AI models using Docker, Kubernetes, and CI/CD pipelines. Implement MLOps workflows for continuous training, monitoring, and model retraining. Optimize AI solutions for cloud platforms (AWS, Azure, GCP) and edge computing. AI Governance & Security: Implement AI safety measures, including adversarial defense and bias mitigation. Ensure compliance with AI regulations (GDPR, HIPAA, SOC 2) and ethical AI principles. Secure AI agents against prompt injection, model poisoning, and adversarial attacks. Required Skills & Qualifications: Technical Skills: Strong programming skills in Python, Java, or R. Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn). Experience with LLMs & NLP (Hugging Face, OpenAI API, BERT, GPT, Claude). Proficiency in multi-agent frameworks (LangChain, CrewAI, AutoGen). Knowledge of reinforcement learning (PPO, A3C, DDPG, SAC). Experience with vector databases (FAISS, Pinecone, Chroma) and knowledge graphs. Strong foundation in MLOps, cloud AI services (AWS, Azure AI, Google Vertex AI). Soft Skills: Strong analytical and problem-solving skills. Ability to work in a fast-paced, cross-functional team. Excellent communication and documentation skills. Preferred Qualifications: Masters. in Computer Science, AI, or Data Science. Certifications in AI/ML (AWS ML Specialist, Google ML Engineer, Microsoft AI Engineer). Experience with autonomous systems, robotics, or digital twins. 5-7 years of similar or equivalent experience. Why Join Us Work on the cutting edge of AI & autonomous agents. Be part of a team driving AI innovation for real-world impact.

Job Overview: We are looking for a highly skilled AI & Agentic Developer to design, build, and deploy intelligent autonomous systems. This role involves developing AI-powered agents that can perceive, reason, and act independently, leveraging LLMs, reinforcement learning, and multi-agent systems. You will work on cutting-edge AI frameworks like LangChain, AutoGen, CrewAI, and MLOps pipelines for scalable deployment. Key Responsibilities: AI & Machine Learning Development: Develop, train, and fine-tune machine learning (ML) and deep learning (DL) models. Implement LLMs, NLP, reinforcement learning (RL), and computer vision (CV) models. Optimize retrieval-augmented generation (RAG) systems with vector databases (FAISS, Pinecone, Chroma). Work on multimodal AI systems integrating text, image, and audio processing. Agentic AI Systems Development: Build autonomous AI agents that can make decisions, learn from feedback, and interact with environments. Implement multi-agent frameworks using LangChain, CrewAI, AutoGen, BabyAGI, MetaGPT. Integrate AI agents with APIs, databases, and enterprise applications for real-world deployment. Software Engineering & MLOps: Design and deploy AI models using Docker, Kubernetes, and CI/CD pipelines. Implement MLOps workflows for continuous training, monitoring, and model retraining. Optimize AI solutions for cloud platforms (AWS, Azure, GCP) and edge computing. AI Governance & Security: Implement AI safety measures, including adversarial defense and bias mitigation. Ensure compliance with AI regulations (GDPR, HIPAA, SOC 2) and ethical AI principles. Secure AI agents against prompt injection, model poisoning, and adversarial attacks. Required Skills & Qualifications: Technical Skills: Strong programming skills in Python, Java, or R. Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn). Experience with LLMs & NLP (Hugging Face, OpenAI API, BERT, GPT, Claude). Proficiency in multi-agent frameworks (LangChain, CrewAI, AutoGen). Knowledge of reinforcement learning (PPO, A3C, DDPG, SAC). Experience with vector databases (FAISS, Pinecone, Chroma) and knowledge graphs. Strong foundation in MLOps, cloud AI services (AWS, Azure AI, Google Vertex AI). Soft Skills: Strong analytical and problem-solving skills. Ability to work in a fast-paced, cross-functional team. Excellent communication and documentation skills. Preferred Qualifications: Masters. in Computer Science, AI, or Data Science. Certifications in AI/ML (AWS ML Specialist, Google ML Engineer, Microsoft AI Engineer). Experience with autonomous systems, robotics, or digital twins. 5-7 years of similar or equivalent experience. Why Join Us Work on the cutting edge of AI & autonomous agents. Be part of a team driving AI innovation for real-world impact.

AI Agentic Developer Job Description Job Overview: We are looking for a highly skilled AI Agentic Developer to design, build, and deploy intelligent autonomous systems. This role involves developing AI-powered agents that can perceive, reason, and act independently, leveraging LLMs, reinforcement learning, and multi-agent systems. You will work on cutting-edge AI frameworks like LangChain, AutoGen, CrewAI, and MLOps pipelines for scalable deployment. Key Responsibilities: AI Machine Learning Development: Develop, train, and fine-tune machine learning (ML) and deep learning (DL) models. Implement LLMs, NLP, reinforcement learning (RL), and computer vision (CV) models. Optimize retrieval-augmented generation (RAG) systems with vector databases (FAISS, Pinecone, Chroma). Work on multimodal AI systems integrating text, image, and audio processing. Agentic AI Systems Development: Build autonomous AI agents that can make decisions, learn from feedback, and interact with environments. Implement multi-agent frameworks using LangChain, CrewAI, AutoGen, BabyAGI, MetaGPT. Integrate AI agents with APIs, databases, and enterprise applications for real- world deployment. Software Engineering MLOps: Design and deploy AI models using Docker, Kubernetes, and CI/CD pipelines. Implement MLOps workflows for continuous training, monitoring, and model retraining. Optimize AI solutions for cloud platforms (AWS, Azure, GCP) and edge computing. AI Governance Security: Implement AI safety measures, including adversarial defense and bias mitigation. Ensure compliance with AI regulations (GDPR, HIPAA, SOC 2) and ethical AI principles. Secure AI agents against prompt injection, model poisoning, and adversarial attacks. Required Skills Qualifications: Technical Skills: Strong programming skills in Python, Java, or R. Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn). Experience with LLMs NLP (API, BERT, Hugging Face, OpenAI GPT, Claude). Proficiency in multi-agent frameworks (LangChain, CrewAI, AutoGen). Knowledge of reinforcement learning (PPO, A3C, DDPG, SAC). Experience with vector databases (FAISS, Pinecone, Chroma) and knowledge graphs. Strong foundation in MLOps, cloud AI services (AWS, Azure AI, Google Vertex AI). Soft Skills: Strong analytical and problem-solving skills. Ability to work in a fast-paced, cross-functional team. Excellent communication and documentation skills. Preferred Qualifications: Masters. in Computer Science, AI, or Data Science. Certifications in AI/ML (AWS ML Specialist, Google ML Engineer, Microsoft AI Engineer). Experience with autonomous systems, robotics, or digital twins. 5-7 years of similar or equivalent experience. Why Join UsWork on the cutting edge of AI autonomous agents. Be part of a team driving AI innovation for real-world impact. Location: Hydrabad,Pune,Noida,Mohali