115 Prompt Engineering Jobs

Key Responsibilities: Prompt Crafting & Optimization Voice & Chat Agent Configuration Quality Assurance Client Collaboration Health insurance Provident fund

About the Role We are looking for a seasonedu00A0Senior AI Agentic Engineeru00A0to lead the development and deployment of autonomous AI agents that leverage large language models (LLMs), advanced memory architectures, and integrated tool use to solve complex real-world problems. You will be responsible for architecting scalable, reliable, and safe agentic systems, driving innovation in autonomous reasoning, and mentoring engineering teams. This role requires a blend of deep technical expertise, leadership skills, and a passion for pushing the boundaries of AI-driven automation. What Youu2019ll Be Doing Lead the design, development, and deployment of autonomous AI agents capable of multi-step reasoning and planning. Architect scalable memory and retrieval systems using vector databases and embeddings to provide long-term context. Build and optimize agent orchestration workflows using techniques like ReAct, Chain-of-Thought, and LangGraph. Collaborate cross-functionally with research, product, and engineering teams to define AI agent capabilities and roadmaps. Drive prompt engineering, fine-tuning, and continual performance improvements of AI agents. Ensure AI safety by implementing monitoring, observability, and risk mitigation strategies. Mentor and support junior engineers, reviewing code and sharing best practices. Oversee cloud infrastructure, containerization, and CI/CD pipelines to ensure scalable, reliable deployments. What You Should Bring Deep expertise in Python and AI/ML system development with a strong focus on autonomous agents. Hands-on experience with LLM APIs such as OpenAI GPT-4, Anthropic Claude, or Google Gemini. Proficiency with agent frameworks like LangChain, AutoGPT, or CrewAI. Strong knowledge of vector databases (FAISS, Pinecone) and embedding models. Skilled in prompt engineering and designing multi-agent workflows. Product mindset with focus on real-world impact and usability. Proven leadership experience including mentoring and collaborating with cross-functional teams. Commitment to AI safety, reliability, and ethical considerations. Strong debugging and problem-solving skills, especially when dealing with unpredictable model outputs or complex agent behavior. Attention to detail around safety and reliability, including awareness of risks like prompt injection, hallucination, and misuse. Strong communication and collaboration skills to work effectively across multiple teams. A proactive, growth-oriented mindset with a high level of intellectual curiosity. Solid understanding of SDLC, CI/CD, and agile methodologies. Commitment to writing secure, performant, and accessible code. Required Skills Advanced Python programming skills for AI/ML applications. Extensive experience with LLM APIs and agentic AI frameworks. Expertise in vector databases and retrieval-augmented generation (RAG). Mastery of prompt engineering techniques: few-shot, CoT, and function calling. Experience integrating external tools and APIs into AI agents. Knowledge of web frameworks like FastAPI or Flask. Proficiency with Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure). Strong debugging, logging, and monitoring skills for AI systems. Excellent communication and leadership capabilities. Preferred Skills Experience with reinforcement learning or multi-agent systems. Background in fine-tuning LLMs or prompt tuning at scale. Contributions to open-source AI projects or published research. Familiarity with AI safety frameworks and bias mitigation strategies. Proficiency with experiment tracking tools like Weights & Biases or LangSmith. AI-Enhanced Development Leverage AI tools like GitHub Copilot to accelerate development workflows, improve code quality, and reduce boilerplate. Demonstrate proficiency in prompt engineering to effectively guide AI tools in generating optimal and context-aware code solutions. Use AI-assisted pair programming to support rapid prototyping, test case generation, and debugging. Stay informed about the evolving landscape of AI-powered development tools and integrate best practices into day-to-day engineering work. Encourage and mentor team members on responsible and secure use of AI in the software development lifecycle. Soft Skills & Traits Strong communication and collaboration skills across cross-functional teams High level of intellectual curiosity and continuous learning mindset Ability to work independently and take ownership of technical solutions Solid understanding of Agile methodologies and SDLC Basic Qualifications Bachelor's degree in Computer Science, Computer Engineering, or a related field. 8+ years of professional experience developing full-stack applications. Proven ability to self-drive and take ownership of technical initiatives. Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form () for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response. Lillyu00A0does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status. #WeAreLilly

Job Summary: We are seeking a highly skilled and innovative Senior AI/ML Engineer with strong experience in building Generative AI solutions from MVP to production. The ideal candidate should have hands-on expertise in multi-agent orchestration, prompt engineering, RAG architecture, and advanced Python frameworks such as LangChain, LangGraph, and AutoGen. Experience with Databricks and tool integrations (e.g., GitHub, UI components) is essential. Key Responsibilities: Design and implement Generative AI solutions, taking projects from MVP to scalable production systems. Build, orchestrate, and optimize multi-agent architectures using frameworks like LangChain, LangGraph, and AutoGen. Integrate external tools and APIs into AI agents (e.g., GitHub, internal/external UI components). Lead the development and deployment of RAG (Retrieval-Augmented Generation) pipelines. Collaborate with cross-functional teams to align AI solutions with business goals. Leverage Databricks for scalable data processing, ML model development, and deployment. Develop, refine, and evaluate prompts for LLM-based tasks (Prompt Engineering). Monitor and optimize the performance and accuracy of AI systems in production. Required Skills & Qualifications: 5+ years of experience in AI/ML, with at least 2 years focused on Generative AI solutions. Strong programming skills in Python and proficiency in GenAI frameworks (LangChain, LangGraph, AutoGen). Experience with building and orchestrating multi-agent systems. Proven track record in taking GenAI-based MVPs to full-scale production. Hands-on experience in integrating AI agents with tools and platforms (e.g., GitHub, custom UIs). Solid understanding and implementation experience with RAG architecture and prompt engineering. Proficiency with Databricks for data and ML workflows. Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment. Nice to Have: Experience with vector databases (e.g., FAISS, Pinecone, Weaviate). Exposure to cloud environments (Azure, AWS, or GCP). Familiarity with CI/CD for ML pipelines.

Looking for Generative AI, NLP, and Deep Learning.Proficient in PyTorch, Python & Prompt Engineering.Hands-on exp. with LLM fine-tuning & GPU acceleration.Previous exp. deploying AI models on the cloud. Candidates from IITs/NITs are highly preferred

Role Value: This role plays a pivotal part in driving innovation through the development of advanced, ML-driven technologies. By aligning business goals with product strategy, the Product Owner helps shape high-impact solutions that bring measurable value to both internal stakeholders and end users. Key Responsibilities: Collaborate closely with Machine Learning and Engineering teams on advanced data extraction technologies (e.g., no-OCR, VLM-based extraction) Translate business needs into epics and user stories for development teams Align product goals with company strategy in coordination with Product Management Maintain a well-prioritized product backlog based on business value Work with stakeholders and data analysts to ensure data-driven product decisions Track product progress, performance, and deliverables across sprints Communicate effectively with globally distributed teams and stakeholders Stay updated with agile practices, ML/AI trends, and industry standards Required Experience & Qualifications: 3+ years as a Product Owner in an agile environment Familiarity with AI/ML patterns (e.g., VLM, LLMs) and experience working with ML-driven teams Technical aptitude with a fast-learning mindset Strong data orientation and analytical thinking Agile and/or SAFe framework knowledge Excellent attention to detail and communication skills Comfortable working in international, cross-functional environments Proficient in spoken and written English Nice to Have: Exposure to Regulatory Compliance Product Marketing experience Strong cross-cultural communication Advanced data analysis capabilities Knowledge of Biometric Security Standards Key Traits: Energetic, teachable, and eager to learn Follows SOPs with accuracy and discipline Collaborative and customer-focused Takes ownership with a proactive, problem-solving attitude Resilient and positive under pressure Role & responsibilities Preferred candidate profile

Experience : 3+ years Shift : (GMT+05:30) Asia/Kolkata (IST) Must have Skills required: Python, Django, Fast API, Prompt Engineering, Apache Airflow, Mongo DB As a Python Developer at Infrrd, your job responsibilities will include: Create python scripts and services. Document inside code and designs. Ensure the deliverables are of the highest quality in terms of functional and technical aspects. Ensure the in sprint defects are closed with the highest quality and any production defects are being taken care of for a specific duration post release. Work along with the PM, Architect and the BA to manage the deliverables from technical and functional aspects Work along with DevOps, and other teams to fulfill roles and see the big picture. Carry out regular code reviews, enforce coding best practices, source control, and deliverables Below is a list of the background we would like our Python Developer to have: 3 -5 years of expertise in Python 3 software engineering Skills Experience building systems that interact with REST APIs or depend on web frameworks like Flask (preferred) or Django. Python Language Proficiency: Strong understanding of Python syntax and libraries like Pandas. Design, Develop, test, and maintain high-quality software using Python programming language. Experience with MongoDB Strong object-oriented design and programming Skills Experience with SQL and NoSQL databases. Proficiency with Git. Nice to Have: Familiarity with Linux. Experience with microservices and architectural design. Experience with Python testing frameworks (unit test, pytest). Knowledge of using OpenAPI specifications for designing REST Endpoints. Experience working with message brokers (RabbitMQ, Redis) Experience in deploying machine learning models in production environments and made them accessible through APIs. Experience with DevOps processes along with Kubernetes and container ecosystem is an advantage. Experience working with AWS services or other cloud services. Skills Python, Django, Fast API, Prompt Engineering, Apache Airflow, Mongo DB

Core Skills & Knowledge: Generative AI Expertise: Understanding of AI Models: Familiarity with various generative AI models like Large Language Models (LLMs), image generation models, and other relevant technologies. Prompt Engineering: Ability to craft effective prompts to guide AI models and achieve desired outcomes. AI Model Fine-Tuning: Knowledge of techniques to customize and improve AI models for specific applications. AI Deployment: Experience in deploying AI models into production environments. Software Development Proficiency: Programming Languages: Strong coding skills in languages like Python, JavaScript, Java, or similar. Software Architecture: Understanding of software architecture principles and design patterns. Databases: Knowledge of database technologies and their integration with AI models. Version Control: Proficiency in using version control systems like Git. Testing and Debugging: Ability to write unit tests, perform integration testing, and debug software issues. Agile Methodologies: Experience with agile development methodologies. Responsibilities : Developing AI-Powered Applications: Designing, developing, and deploying AI-powered applications and features. Integrating AI into Existing Systems: Seamlessly integrating AI technologies into existing software systems. Building AI Co-Pilots: Creating AI co-pilot solutions that augment human capabilities. Data Security and Privacy: Ensuring the responsible and ethical deployment of AI solutions. Collaboration: Working with cross-functional teams, including AI researchers, product managers, and other software engineers.

Prompt Engineer. Are you a seasoned Pythonista with a knack for crafting perfect prompts? We're looking for a talented and experienced Prompt Engineer to join our team! In this role, you'll be instrumental in developing and refining the prompts that drive our cutting-edge AI models. You'll work closely with stakeholders, leveraging your deep understanding of Python and prompt engineering to optimize model performance and ensure clear, effective communication with our systems. Mandatory Skills- Prompt Engineering, Python What you'll bring: * 5-7 years of experience in Python development, with a strong grasp of its ecosystem and best practices. * 3-5 years of experience in Prompt Engineering , demonstrating a proven ability to design, test, and refine prompts for various AI models. * Very good knowledge of GHCP (Google Cloud's AI Platform , including Generative AI capabilities). * Excellent communication and stakeholder management skills, with the ability to translate complex technical concepts into clear, actionable insights for diverse audiences. If you're passionate about the future of AI and thrive in a collaborative environment, we encourage you to apply! Share your profile on Aarushi.Shukla@coforge.com

Expected Notice Period: 30 Days Shift: (GMT+05:30) Asia/Kolkata (IST) What do you need for this opportunity Must have skills required: Agent Orchestration, AI-powered SaaS, open-source projects, Prompt Engineering, Python, Micro services, Rest APIs, LLM, Ai products, Amazon AWS, Cloud Services Lyzr is Looking for: What Youll Do : Design and develop backend services that power GenAI applications and platforms Build APIs and infrastructure to support LLM-based features, tools, and agent workflows Collaborate with frontend, ML, and product teams to create seamless AI experiences Optimize backend systems for performance, scalability, and reliability Work with vector databases, data pipelines, and AI model endpoints (OpenAI, Anthropic, Hugging Face, etc.) Maintain clean, secure, and production-grade code Contribute to the architecture and roadmap of our GenAI stack What Were Looking For : 13 years of experience in backend development Proficient in Python Strong experience with REST APIs, microservices, and scalable cloud infrastructure Experience building or supporting GenAI applications and/or LLM-powered products Comfortable working with vector databases (e.g., Pinecone, Weaviate, FAISS) and RAG pipelines Exposure to cloud services (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) Excellent problem-solving, communication, and collaboration skills Nice to Have Experience building and scaling AI-powered SaaS products Contributions to open-source GenAI projects Understanding of prompt engineering and agent orchestration frameworks

Working knowledge of Azure AI Working knowledge of LLM , RAG- Data insights , Reasoning Agent , Chain of thought Knowledge of Graph DB , COSMOS DB , Neo4j Prompt engineering API building Domain knowledge in Finance , data management Should have worked on enterprise AI use cases and one go live solution.

Part-time - Personal Injury Paralegal (AI-Generated Settlement Demand Reviewer) Remote - India Employment Type: Part time Part-Time (10-20 hours per week, flexible schedule) Position Overview Were developing an AI-driven agent to draft settlement demand notes, and we need an experienced Personal Injury Paralegal to review, enhance, and validate the AIs outputs. The ideal candidate will have a strong background in personal injury settlement preparation, exceptional attention to detail, and the ability to identify and correct inaccuracies in AI-generated content. Youll work closely with our legal team and AI engineers to ensure every demand package is accurate, persuasive, and fully compliant with jurisdictional standards. Key Responsibilities Review AI-Generated Drafts Examine settlement demand notes produced by the AI agent for accuracy, completeness, and proper organization. Identify errors or omissions in liability narratives, damage calculations, medical summaries, and policy-limit references. Enhance language for clarity, professionalism, and persuasive impact, ensuring tone aligns with firm standards. Content Improvement & Validation Verify that medical records, bills, wage-loss documentation, and expert opinions are correctly interpreted and summarized Cross-check all numerical values (medical expenses, lost wages, future-care estimates) against source documents. Confirm that statutory deadlines, comparative fault percentages, and damage caps are properly applied. Case File Management Organize and maintain case files (electronic and/or hard copy) so supporting documents align with AI-generated narratives. Tag any missing or inconsistent data points and liaise with attorneys or medical providers to procure necessary information. Legal Research & Compliance Research relevant case law, local court rules, and insurance adjuster guidelines to ensure demand letters meet current jurisdictional requirements. Advise on any new legal developments that should be incorporated into automated templates. Collaboration & Feedback Loop Provide structured feedback to AI engineers regarding common errors, formatting improvements, or language enhancements. Assist in refining prompts and templates to improve future AI drafts. Participate in weekly alignment meetings with attorneys, AI developers, and project managers to review quality metrics and set improvement goals. Drafting & Correspondence After AI review, prepare final settlement demand packagescombining improved narrative, medical summaries, and attachmentsfor attorney approval. Draft follow-up correspondence to insurance adjusters, defense counsel, and expert witnesses when supplemental documentation or clarifications are needed. Quality Control & Reporting Maintain a checklist for each demand package to ensure all elements (liability analysis, damages breakdown, supporting exhibits) are present and accurate. Generate weekly reports tracking AI accuracy scores, number of revisions, and turnaround times. Flag any systemic AI issues to the legal and engineering teams for corrective action. Required Qualifications Minimum 5 years of hands-on Personal Injury Paralegal experiencespecifically with settlement demand preparation and review. Certificate or Associates degree in Paralegal Studies (preferred). Proficiency with case management systems (e.g., Clio, Filevine, MyCase) and document-review platforms. Strong command of medical terminology, billing codes, and damage calculations. Proven ability to draft clear, persuasive settlement demand letters. Excellent written and verbal communication skills with meticulous attention to detail. Experience working with or reviewing AI-generated legal documents (preferred but not mandatory). Ability to work independently, manage deadlines, and juggle multiple files in a fast-paced environment. Reliable internet connection and quiet workspace (for remote work). Desired Skills & Attributes Familiarity with insurance adjuster protocols, and jurisdiction-specific settlement practices. Basic understanding of prompt engineering or experience collaborating with AI/ML teams Advanced Excel skills for damage-analysis spreadsheets. Strong critical thinking and problem-solving aptitudeable to spot subtle inaccuracies or inconsistencies in complex data sets. High level of discretion and ability to maintain confidentiality of client information. Collaborative mindsetcomfortable providing structured feedback to both attorneys and software engineers. Work Schedule & Compensation Schedule: 7:30 PM 11:30 PM , approximately 10-20 hours per week. Occasional urgent requests may require same-day review. Compensation: Competitive hourly rate, commensurate with experience. Benefits:N/A (part-time, hourly). Potential to expand hours or take on additional projects based on performance and firm needs. Location- Remote, Delhi NCR, Bangalore, Chennai, Pune, Kolkata, Ahmedabad, Mumbai, Hyderabad

Experience Required We are looking for an experienced AI Engineer to join our team. The ideal candidate will have a strong background in designing, deploying, and maintaining advanced AI/ML models with expertise in Natural Language Processing (NLP), Computer Vision, and architectures like Transformers and Diffusion Models. You will play a key role in developing AI-powered solutions, optimizing performance, and deploying and managing models in production environments. Key Responsibilities 1. AI Model Development and Optimization: Design, train, and fine-tune AI models for NLP, Computer Vision, and other domains using frameworks like TensorFlow and PyTorch. Work on advanced architectures, including Transformer-based models (e.g., BERT, GPT, T5) for NLP tasks and CNN-based models (e.g., YOLO, VGG, ResNet) for Computer Vision applications. Utilize techniques like PEFT (Parameter-Efficient Fine-Tuning) and SFT (Supervised Fine-Tuning) to optimize models for specific tasks. Build and train RLHF (Reinforcement Learning with Human Feedback) and RL-based models to align AI behavior with real-world objectives., Explore multimodal AI solutions combining text, vision, and audio using generative deep learning architectures. 2. Natural Language Processing (NLP): Develop and deploy NLP solutions, including language models, text generation, sentiment analysis, and text-to-speech systems. Leverage advanced Transformer architectures (e.g., BERT, GPT, T5) for NLP tasks. 3. AI Model Deployment and Frameworks: Deploy AI models using frameworks like VLLM, Docker, and MLFlow in production-grade environments. Create robust data pipelines for training, testing, and inference workflows. Implement CI/CD pipelines for seamless integration and deployment of AI solutions. 4. Production Environment Management: Deploy, monitor, and manage AI models in production, ensuring performance, reliability, and scalability. Set up monitoring systems using Prometheus to track metrics like latency, throughput, and model drift. 5. Data Engineering and Pipelines: Design and implement efficient data pipelines for preprocessing, cleaning, and transformation of large datasets. Integrate with cloud-based data storage and retrieval systems for seamless AI workflows. 6. Performance Monitoring and Optimization: Optimize AI model performance through hyperparameter tuning and algorithmic improvements. Monitor performance using tools like Prometheus, tracking key metrics (e.g., latency, accuracy, model drift, error rates etc.) 7. Solution Design and Architecture: Collaborate with cross-functional teams to understand business requirements and translate them into scalable, efficient AI/ML solutions. Design end-to-end AI systems, including data pipelines, model training workflows, and deployment architectures, ensuring alignment with business objectives and technical constraints. Conduct feasibility studies and proof-of-concepts (PoCs) for emerging technologies to evaluate their applicability to specific use cases. 8. Stakeholder Engagement: Act as the technical point of contact for AI/ML projects, managing expectations and aligning deliverables with timelines. Participate in workshops, demos, and client discussions to showcase AI capabilities and align solutions with client needs. Technical Skills Proficient in Python, with strong knowledge of libraries like NumPy, Pandas, SciPy, and Matplotlib for data manipulation and visualization. Expertise in TensorFlow, PyTorch, Scikit-learn, and Keras for building, training, and optimizing machine learning and deep learning models. Hands-on experience with Transformer libraries like Hugging Face Transformers, OpenAI APIs, and LangChain for NLP tasks. Practical knowledge of CNN architectures (e.g., YOLO, ResNet, VGG) and Vision Transformers (ViT) for Computer Vision applications. Proficiency in developing and deploying Diffusion Models like Stable Diffusion, SDX, and other generative AI frameworks. Experience with RLHF (Reinforcement Learning with Human Feedback) and reinforcement learning algorithms for optimizing AI behaviors. Proficiency with Docker and Kubernetes for containerization and orchestration of AI workflows. Hands-on experience with MLOps tools such as MLFlow for model tracking and CI/CD integration in AI pipelines. Expertise in setting up monitoring tools like Prometheus and Grafana to track model performance, latency, throughput, and drift. Knowledge of performance optimization techniques, such as quantization, pruning, and knowledge distillation, to improve model efficiency. Experience in building data pipelines for preprocessing, cleaning, and transforming large datasets using tools like Apache Airflow, Luigi Familiarity with cloud-based storage systems (e.g., AWS S3, Google BigQuery) for efficient data handling in AI workflows. Strong understanding of cloud platforms (AWS, GCP, Azure) for deploying and scaling AI solutions. Knowledge of advanced search technologies such as Elasticsearch for indexing and querying large datasets. Familiarity with edge deployment frameworks and optimization for resource-constrained environments.

BMC is looking for a Java Tech Lead, an innovator at heart, to join a team of highly skilled software developers, responsible for BMCs Helix Capacity Optimization product. Here is how, through this exciting role, YOU will contribute to BMC's and your own success: Play a vital role in project design to ensure scalability, reliability, and performance are met Design and develop new features as well as maintain existing features by adding improvements and fixing defects in complex areas (using Java) Design and maintain robust AI/ML pipelines using Python and industry-standard ML frameworks . Collaborate closely with AI researchers and data scientists to implement, test, and deploy machine learning models in production. Leverage prompt engineering techniques and implement LLM optimization strategies to enhance response quality and performance. Assist in troubleshooting complex technical problems in development and production Implement methodologies, processes & tools Initiate projects and ideas to improve the teams results On-board and mentor new employees To ensure youre set up for success, you will bring the following skillset & experience: Backend Development: FastAPI, RESTful APIs, Python Cloud Infrastructure: AWS, EKS, Docker, Kubernetes AI/ML Frameworks: LangChain, Scikit-learn, Bedrock, Hugging Face (optional to add if applicable) ML Pipelines: Python, Pandas, NumPy, joblib DevOps & CI/CD: Git, Terraform (optional), Helm, GitHub Actions LLM Expertise: Prompt engineering, RAG (Retrieval Augmented Generation), vector databases (e.g., FAISS, Pinecone) You have 12+ years of experience in Java Backend development You have experience as a Backend Tech Lead You have experience in Spring, Swagger, REST API You worked with Spring Boot, Docker, Kubernetes You are a self-learner whos passionate about problem solving and technology You are a team player with good communication skills in English (verbal and written) Whilst these are nice to have, our team can help you develop in the following skills: Public Cloud (AWS, Azure, GCP) Python, Node.js, C/C++ Automation Frameworks such as Robot Framework

Key Responsibilities: As an Infra AI Automation Programmer you will work on design development and deployment of AI systems that generate content or data often using techniques such as deep learning neural networks and generative models using Python backend coding Meet with clients in and remotely to understand their business objectives challenges and identify potential use cases for Generative AI Assess the client s existing technology infrastructure data landscape and AI maturity to determine the feasibility and best approaches for Gen AI adoption Conduct workshops and discovery sessions to brainstorm Gen AI applications across various business functions e g content creation customer service product development marketing Gen AI Strategy and Solution Design Develop tailored Gen AI strategies and roadmaps for clients in outlining specific use cases implementation plans and expected business outcomes Design Gen AI solutions architecture considering factors such as model selection e g large language models LLMs diffusion models data requirements integration with existing systems and scalability Evaluate and recommend appropriate Gen AI platforms tools and APIs e g OpenAI Google AI Azure AI open source libraries relevant to the client s needs and the Indian market Advise on prompt engineering techniques to optimize the output and performance of Gen AI models for specific client applications Technical Requirements: At least 2 years of programming experience in Python Handson experience in working on Gen AI Handson experience in Tensorflow Pytorch Langchain and Prompt Engineering Experience with vector databases and retrieval augmented generation RAG Familiarity with MLOps principles and tools for deploying and managing Gen AI models Understanding of ethical considerations and responsible AI frameworks Understanding of data structures data modelling SQL NOSQL Object oriented and functional programming GIT Knowledge of basic algorithms and object oriented and functional design principles Deep Knowledge of Mathematics Probability Statistics and Algorithms Good Communication skills Good analytical and problem solving skills Additional Responsibilities: Besides the professional qualifications of the candidates we place great importance in addition to various forms personality profile These include High analytical skills A high degree of initiative and flexibility High customer orientation High quality awareness Excellent verbal and written communication skills Preferred Skills: Technology->Machine Learning->Python,Technology->Machine Learning->TensorFlow,Technology->Machine Learning->Generative AI

An Ideal Candidate: 5+ years in software development; 2+ years in AI tools/frameworks. Technical Skills: Python and .NET development. Multi-agent orchestration (e.g., semantic kernel, autogen). RAG techniques (e.g., GraphRAG). Expertise in Model Context Protocol (MCP). Experience around developing autonomous coding agents and research agents. Preferred Qualifications Experience with distributed systems and cloud-based AI architectures. Knowledge in NLP, knowledge graphs, and semantic reasoning. Experience with Azure DevOps and Azure Cloud Key Competencies: 1.Seeking an AI Developer with a software development background, expertise in AI tools, multi-agent orchestration frameworks, RAG techniques, and Model Context Protocol (MCP). Proficiency in Python, .NET, and hands-on experience with coding agents and research agents is preferable. 2. Responsibilities Build and optimize AI systems using semantic kernel, autogen, and multi-agent frameworks. Develop solutions leveraging retrieval-augmented generation (RAG), preferably GraphRAG. Integrate and optimize workflows using Model Context Protocol (MCP). Write scalable and maintainable code in Python and .NET. Design autonomous coding agents and research agents for problem-solving. 3.Research emerging AI tools and methods for practical application. 4.Debug, troubleshoot, and optimize AI systems for performance and reliability. Ref:JN-062025-794725

Seeking a PHP Full Stack Developer with 3-4 years experience in scalable app development. Must be proficient in frontend & backend tech, and passionate about AI tools and prompt engineering to boost productivity and innovation.

Seeking a Full Stack Python Developer with 3+ years experience in scalable app development. Must be proficient in frontend & backend tech, and passionate about AI tools and prompt engineering to boost productivity and innovation.

Role: ML Engineer. Exp : 5 Years to 10 Years Location : Hyderabad. Job Overview: Were seeking a ML Engineer / Data Scientist to architect agentic AI solutions and own the full ML lifecycle—from proof-of-concept to production. You’ll operationalize LLMs, build agentic workflows, implement MLOps best practices, and design multi-agent systems for cybersecurity tasks. Key Responsibilities: Operationalize large language models and agentic workflows (LangChain, LangGraph, LlamaIndex) to automate security decision-making and threat response. Design, deploy, and maintain multi-agent AI systems for log analysis, anomaly detection, and incident response. Build proof-of-concept GenAI solutions and evolve them into production-ready components on AWS (Bedrock, SageMaker, Lambda, EKS/ECS) using reusable best practices. Implement CI/CD pipelines for model training, validation, and deployment with GitHub Actions, Jenkins, and AWS CodePipeline. Manage model versioning with MLflow and DVC, set up automated testing, rollback procedures, and retraining workflows. Automate cloud infrastructure provisioning with Terraform and develop REST APIs and microservices containerized with Docker and Kubernetes. Monitor models and infrastructure through CloudWatch, Prometheus, and Grafana; analyze performance and optimize costs and SLA compliance. Collaborate with data scientists, application developers, and security analysts to integrate agentic AI into existing security workflows. Qualifications: Bachelor’s or master’s in computer science, Data Science, AI or related quantitative discipline. 4+ years of software development experience, including 3+ years building and deploying LLM-based/agentic AI architectures. In-depth knowledge of generative AI fundamentals (LLMs, embeddings, vector databases, prompt engineering, RAG). Hands-on experience with LangChain, LangGraph, LlamaIndex, Crew.AI or equivalent agentic frameworks. Strong proficiency in Python and production-grade coding for data pipelines and AI workflows. Deep MLOps knowledge: CI/CD for ML, model monitoring, automated retraining, and production-quality best practices. Extensive AWS experience with Bedrock, SageMaker, Lambda, EKS/ECS, S3 (Athena, Glue, Snowflake preferred). Infrastructure as Code skills with Terraform. Experience building REST APIs, microservices, and containerization with Docker and Kubernetes. Solid data science fundamentals: feature engineering, model evaluation, data ingestion. Understanding of cybersecurity principles, SIEM data, and incident response. Excellent communication skills for both technical and non-technical audiences. Preferred Qualifications: AWS certifications (Solutions Architect, Developer Associate). Nice to have Experience with Model Context Protocol (MCP) and RAG integrations. Nice to have Experience in Crew.AI Familiarity with workflow orchestration tools (Apache Airflow). Experience with time series analysis, anomaly detection, and machine learning.

Fast track growth & PnL ownership Handling multiple clients Job Description Proficiency in designing and building REST APIs. Strong understanding of any Cloud platforms such as - GCP/AWS Experience with NoSQL DB or Relational DB Experience with unit testing in any testing framework and maintaining 100% code coverage. Profound knowledge in Serverless design pattern Familiarity with Django or Webapp2 or Flask or similar web app frameworks Experience collaborating with product and design teams. Familiarity with integrating third-party solutions. The Successful Applicant Educational background includes a degree (B.E/B.Tech/M.Tech) in Computer Science, Engineering, or a related field. 8+ years experience as a backend developement. Good understanding in Google cloud platform Knowledge of AI and how to leverage it for day-to-day tasks in backend development. Familiarity with prompt engineering to enhance productivity. Prior experience working with global or regional teams. Experience with agile methodologies and working within cross-functional teams. What's on Offer We've come to realize that we're not merely in the B2B Food service industry we're in the business of Capturing Hearts. We find ourselves in a unique position to turn ordinary, mundane corporate cafeterias into places of pure delight, where individuals can freely express themselves, find inspiration, and share happiness. Such a profound transformation opportunity is a rare gift, where the purpose of our enterprise transcends the ordinary. If this vision resonates with you, we invite you to join us in our mission to spread joy and happiness in a world weighed down by stress and pressure. Together, let's play our part in making this world a more beautiful place.

Genpact (NYSE: G) is a global professional services and solutions firm delivering outcomes that shape the future. Our 125,000+ people across 30+ countries are driven by our innate curiosity, entrepreneurial agility, and desire to create lasting value for clients. Powered by our purpose - the relentless pursuit of a world that works better for people - we serve and transform leading enterprises, including the Fortune Global 500, with our deep business and industry knowledge, digital operations services, and expertise in data, technology, and AI. Inviting applications for the role of Principal Consultant - Generative AI In this role, you will be responsible for creating cutting-edge artificial intelligence solutions that possess a broad understanding of diverse tasks and domains. You will contribute to the development of intelligent systems capable of adapting and learning across a wide range of applications. Responsibilities . experience in Cloud & Generative AI . Hands-on knowledge in Large Language Models from OpenAI, Meta, Google, Hugging Face etc. - demonstrate ability to implement solutions leveraging LLMs . Working knowledge of generative models, prompt engineering, fine-tuning - to implement innovative AI use cases . Collaborate with the architect to develop prototypes, conduct PoCs . Collaborate with service line teams to design, implement and manage Gen-AI solutions . Define and document best practices and strategies regarding deployment and infrastructure maintenance . Good understanding of AWS/Azure primarily focus on AI ecosystems such as Elasticsearch, Azure OpenAI/Cognitive services . Ability to write robust code in Python Java, React, RDBMS (Postgres) - Transactional and Analytical databases . (More than 1 language including Python is preferred) . Experience with Docker and Kubernetes . Experience with Agile Development. Qualifications we seek in you! Minimum Qualifications / Skills . BE/B.TECH . Strong understanding of generative AI models and algorithms. . Proficiency in at least one Generative AI Model (OpenAI ChatGPT, llama, Claude 2, etc.). Preferred Qualifications/ Skills . Self-starter . Highly motivated/passionate for new technologies, engineering mindset . Strong teamwork, problem-solving, and analytical skills . Good communication Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Get to know us at www.genpact.com and on X, Facebook, LinkedIn, and YouTube. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

About the Role We are seeking a talented AI Engineer with 2-3 years of professional experience to join our growing team. As a AI Engineer, you will collaborate with data scientists, software engineers, and product teams to design, build, and deploy ML models that drive value across our organisation. This is an excellent opportunity for someone early in their ML career who wants to gain hands-on experience with real-world applications while working alongside experienced professionals. Responsibilities Implement and optimize ML algorithms and models based on requirements from data scientists and product teams Work with Large Language Models (LLMs) to develop AI-powered applications and features Design prompts, fine-tune, and evaluate LLM performance for specific use cases Build systems to integrate LLMs with existing products and services Design and develop ML/DL pipelines for data preprocessing, model training, evaluation, and deployment Experience in using RAG Collaborate with software engineers to integrate ML/DL models into production systems Monitor and maintain deployed ML/DL models, ensuring reliability and performance Participate in code reviews and contribute to engineering best practices Stay current with the latest ML/DL research and technologies, evaluating their potential application to business problems Assist in collecting and preparing datasets for model training and validation Document model architecture, data flows, and deployment procedures Strong in Machine Learning and Deep learning Basics Requirements Bachelor's degree in Computer Science, Statistics, Applied Mathematics, or a related field (Master's preferred but not required) 1-2 years of professional experience building and deploying machine learning models Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn Experience with Large Language Models (LLMs), including understanding of transformer architectures, fine-tuning approaches, and prompt engineering Knowledge of NLP concepts and techniques applicable to LLMs (embeddings, tokens, context windows, etc.) Experience with data processing libraries like Pandas, NumPy, and visualization tools like Matplotlib or Seaborn Understanding of ML fundamentals, including supervised/unsupervised learning, feature engineering, and model evaluation Knowledge of software engineering best practices (version control, testing, CI/CD) Basic understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker)

As Lead ML Engineer , you'll lead the development of predictive models for demand forecasting, customer segmentation, and retail optimization, from feature engineering through deployment. As Lead ML Engineer, you'll lead the development of predictive models for demand forecasting, customer segmentation, and retail optimization, from feature engineering through deployment. Responsibilities: Build and deploy models for forecasting and optimization Perform time-series analysis, classification, and regression Monitor model performance and integrate feedback loops Use AWS SageMaker, MLflow, and explainability tools (e.g., SHAP or LIME)

We are seeking a passionate and driven Generative AI Engineer to design, develop, and deploy innovative AI applications using Large Language Models (LLMs). The ideal candidate will have hands-on experience with prompt engineering, Python programming, and foundational knowledge of AI orchestration frameworks like LangChain and LangGraph. Experience with the Model Context Protocol (MCP) is a significant advantage. You will work closely with senior AI engineers and cross-functional teams to bring cutting-edge generative AI solutions to life Role & responsibilities Generative AI Application Development: Design, build, and deploy AI applications leveraging LLMs for various use cases such as RAG (Retrieval-Augmented Generation) based chatbots, intelligent agents, content generation, and more. Prompt Engineering: Develop, test, and optimize advanced prompt engineering techniques (e.g., few-shot, Chain-of-Thought, ReAct) to enhance LLM performance, accuracy, and user experience. LLM Orchestration with LangChain & LangGraph: Utilize LangChain and LangGraph to build robust, stateful, and multi-agent AI systems, managing complex conversational flows and integrating external tools and data sources. Python Development: Write clean, efficient, and well-documented Python code for AI model integration, data preprocessing, API development, and general application logic. Model Context Protocol (MCP): Implement and leverage MCP for seamless interaction between LLMs and external systems, ensuring efficient context management and secure communication (if applicable to project needs). Model Evaluation & Testing: Assist in evaluating the performance of generative AI models, defining relevant metrics, and conducting rigorous testing to ensure reliability and effectiveness. Collaboration: Work collaboratively with data scientists, machine learning engineers, product managers, and other stakeholders to translate business requirements into technical solutions. Research & Innovation: Stay updated with the latest advancements in generative AI, LLMs, and related frameworks, and actively experiment with new approaches to improve existing solutions. Deployment & MLOps (Basic): Contribute to the deployment of AI models and understand basic MLOps practices for model versioning, monitoring, and continuous improvement. . Required Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related quantitative field. 1-3 years of hands-on experience in machine learning, software development, or data science, with exposure to AI/ML projects. Strong proficiency in Python programming and familiarity with relevant libraries (e.g., NumPy, Pandas). Proven experience with LangChain for building LLM-powered applications and chaining together various components. Familiarity with LangGraph for orchestrating complex, stateful multi-agent workflows. Hands-on experience with prompt engineering and optimizing prompts for LLMs. Understanding of Large Language Models (LLMs) and their underlying concepts. Experience or strong understanding of Model Context Protocol (MCP) for connecting LLMs with external tools and data sources. Excellent problem-solving skills and a proactive approach to challenges. Strong communication and interpersonal skills, with the ability to articulate technical concepts clearly.) Preferred Skills : Experience with cloud platforms (Azure, AWS, GCP) for deploying AI/ML solutions. Microsoft Certified: Azure AI Engineer Associate (AI-102) or similar AI/ML certification. Familiarity with vector databases (e.g., Pinecone, Chroma, FAISS). Experience with deep learning frameworks such as TensorFlow or PyTorch. Knowledge of Natural Language Processing (NLP) techniques beyond LLMs. Experience in building conversational AI systems or chatbots

Role & responsibilities We are looking for Senior Gen AI Engineer permanent position with MNC company for Remote. Preferred candidate profile Senior Gen AI Engineer GenAI / ML (Python, Langchain) Full Time Focus: Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Key Responsibilities: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions. Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives. Translate business needs into scalable, production-ready AI solutions. Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases. Contribute to architecture discussions, code reviews, and performance optimization. Skills Required: Proficient in Python, Langchain, and SQL. Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows. Background in machine learning or software engineering with a focus on system-level thinking. Experience working with cloud platforms like AWS, Azure, or GCP. Ability to work independently while collaborating effectively across teams. Excellent communication and stakeholder management skills. Preferred Qualifications: Hands-on experience in LLMs and Generative AI techniques. Experience contributing to ML/AI product pipelines or end-to-end deployments. Familiarity with MLOps and scalable deployment patterns for AI models. Prior exposure to client-facing projects or cross-functional AI teams.

Role & responsibilities Strong understanding of machine learning principles Familiarity with deep learning concepts and frameworks Development, training, deployment and fine tuning of large language models Azure Open AI, Prompt Engineering, LangGraph Agentic Interested share your details to uthirakumar.k@tekskills.in Full Name as Per Pan Card : Email id - Contact number: Alternative Number and Mail ID : Total Exp in IT industry: Relevant experience (Particular skill as per JD): Current CTC ( fixed and variable) : Expected CTC (in INR): Official Notice Period: If Serving Notice Period When is your LWD: Any offers in hand?: (Y/N): Do you have any career Gap?: Current Location: Preferred Location: Reason for job change: