74 Crewai Jobs - Page 2

Prompt Engineering and its strategies Should know how LLM works and its hyper parameter ( temperature, top_p) Agentic AI framework like langchain, lang graph, crew ai RAG LLM evaluation and observability Python Deployment on any one of cloud services

As a part of the global leader in Cloud, AI, and Digital technologies, you will be responsible for leveraging GenAI models and frameworks to enhance the efficiency and productivity of businesses worldwide. Your role will involve serving as the primary technical expert for GenAI technologies, focusing on developing, implementing, and optimizing advanced AI models and solutions to expand our GenAI footprint across clients. You will provide technical leadership, drive innovation, and ensure the successful integration of AI technologies into our IT services for clients. Your responsibilities will include defining best practices, standards, and templates for AI solutions, conducting workshops, demos, and client presentations on GenAI use cases, and collaborating closely with cross-functional teams to identify, evaluate, and implement use cases across different customers. In addition, you will evaluate and integrate emerging GenAI technologies to enhance conversational AI capabilities, provide technical leadership, mentorship, and guidance to teams, troubleshoot and resolve complex platform integration and functionality issues. You will also be hands-on in independently building quick prototypes and demonstrating them to key decision-makers. To be successful in this role, you should have 10+ years of total IT experience with at least 3-4 years of hands-on experience with AI/GenAI technologies. Expertise in NLP, NLU, NLQ, and NLG technologies is essential, along with Core Python proficiency to build applications and engineer GenAI solutions at an enterprise scale. You must have delivered production-grade projects on agentic AI frameworks, as well as hands-on experience with agentic AI frameworks such as Autogen, Langraph, or Crew.AI. Experience with NLQ to SQL-based GenAI applications using core Python tools and frameworks, databases, data warehouses, and data lakes on cloud platforms is required. Deep knowledge of architecting product-scale GenAI applications on Microsoft Azure Cloud, AWS Cloud, or GCP cloud environments is a must. Strong programming skills in Python, Node.js, Java, or similar, API programming, familiarity with LLMs, prompt engineering, fine-tuning, and embeddings are desired. Excellent problem-solving skills, solution architecture capabilities, and client-facing communication skills are essential. The ability to translate business needs into technical solutions, manage multiple projects and priorities in a fast-paced environment, and work with cloud platforms is crucial. Moreover, expertise in building technical proposals with winning engineering architectures and technical articulation for CXO level audience, and experience in developing fast PoVs and blueprints on new and emerging GenAI models, tools, frameworks, and other stack will be advantageous for this role.,

At InnovationM , we're shaping the future of enterprise AI and are looking for a Senior Agentic AI Engineer to lead the design and development of cutting-edge agentic frameworks. Youll help build a scalable AI foundation that empowers multiple teams to deploy intelligent agents with autonomy, memory, and reasoning capabilities. Key Skills: Python | LangGraph | CrewAI | RAG | LLMs | Agentic Frameworks Tool Integration | Vector Databases | Autonomous Decision-Making Human-in-the-Loop | Secure, Scalable Architectures What Youll Do: Architect enterprise-grade AI frameworks Build reusable agentic patterns using LangGraph, CrewAI, AutoGPT Enable tools like APIs, vector stores, RPA, and internal services Collaborate across teams to bring autonomy into complex workflows Mentor and guide teams on the future of agentic AI Why Join Us? Work on the latest in AI/ML and agentic systems Engage with large-scale clients and impactful projects Thrive in a culture of innovation, learning & development Location: Noida Sec-126 l 5 Days work from Office Apply now: nikita.gautam@innovationm.com

Job Title: Software Engineer Job Type: Full-Time Experience Level: Mid-Level (4 - 6 years) What You Can Expect from the Job As a Mid-Level AI Engineer focused on Large Language Model (LLM) development , you will: Build and optimize LLM-powered applications using LangChain , CrewAI , and other orchestration tools. Implement context management protocols to improve model memory and performance. Design and deploy user-friendly interfaces using OpenWebUI for interacting with LLMs. Prepare and manage datasets for training, fine-tuning, and inference. Integrate vector databases (e.g., FAISS, Pinecone) and retrieval-augmented generation (RAG) pipelines. Collaborate with cross-functional teams on prompt engineering, agent workflows, and intelligent automation. Stay current with the latest developments in LLMs, open-source tools, and AI research. Contribute to scalable, ethical, and production-ready AI systems. What Will Help You Do the Job Well Must have skills/characteristics 46 years of experience in AI/ML engineering, with a focus on NLP or LLMs. Strong proficiency in Python and experience with frameworks like LangChain , CrewAI , and Transformers (Hugging Face) . Hands-on experience with OpenWebUI for deploying and managing LLM interfaces. Solid understanding of model context protocols , token limits, and memory handling. Experience in data preparation , including cleaning, labelling, and formatting for LLM workflows. Familiarity with vector databases , embedding models , and retrieval systems . A collaborative mindset and the ability to work in fast-paced, agile environments. Other desirable skills/characteristics Experience with agent-based architectures and multi-agent coordination. Knowledge of LLMOps , model deployment, and monitoring tools. Familiarity with Microsoft Co-pilot and its integration into enterprise workflows. Contributions to open-source AI projects or research publications. Knowledge if ISMS Principles and best practices. Willingness to Travel domestic/international. Thank you for your interest with CCS. We hope you find a meaningful career! Please send your profiles to careers@ccs-technologies.com We will Trust, Recognize, Care You will – Learn, Contribute, Grow

As an AI Research Scientist, your role will involve developing the overarching technical vision for AI systems that cater to both current and future business needs. You will be responsible for architecting end-to-end AI applications, ensuring seamless integration with legacy systems, enterprise data platforms, and microservices. Collaborating closely with business analysts and domain experts, you will translate business objectives into technical requirements and AI-driven solutions. Working in partnership with product management, you will design agile project roadmaps that align technical strategy with market needs. Additionally, you will coordinate with data engineering teams to guarantee smooth data flows, quality, and governance across various data sources. Your responsibilities will also include leading the design of reference architectures, roadmaps, and best practices for AI applications. You will evaluate emerging technologies and methodologies, recommending innovations that can be integrated into the organizational strategy. Identifying and defining system components such as data ingestion pipelines, model training environments, CI/CD frameworks, and monitoring systems will be crucial aspects of your role. Leveraging containerization (Docker, Kubernetes) and cloud services, you will streamline the deployment and scaling of AI systems. Implementing robust versioning, rollback, and monitoring mechanisms to ensure system stability, reliability, and performance will also be part of your duties. Project management will be a key component of your role, overseeing the planning, execution, and delivery of AI and ML applications within budget and timeline constraints. You will be responsible for the entire lifecycle of AI application development, from conceptualization and design to development, testing, deployment, and post-production optimization. Enforcing security best practices throughout each phase of development, with a focus on data privacy, user security, and risk mitigation, will be essential. Furthermore, providing mentorship to engineering teams and fostering a culture of continuous learning will play a significant role in your responsibilities. In terms of mandatory technical and functional skills, you should possess a strong background in working with or developing agents using langgraph, autogen, and CrewAI. Proficiency in Python, along with robust knowledge of machine learning libraries such as TensorFlow, PyTorch, and Keras, is required. You should also have proven experience with cloud computing platforms (AWS, Azure, Google Cloud Platform) for building and deploying scalable AI solutions. Hands-on skills with containerization (Docker), orchestration frameworks (Kubernetes), and related DevOps tools like Jenkins and GitLab CI/CD are necessary. Experience using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation to automate cloud deployments is essential. Additionally, proficiency in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra) and expertise in designing distributed systems, RESTful APIs, GraphQL integrations, and microservices architecture are vital for this role. Knowledge of event-driven architectures and message brokers (e.g., RabbitMQ, Apache Kafka) is also required to support robust inter-system communications. Preferred technical and functional skills include experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to ensure system reliability and operational performance. Familiarity with cutting-edge libraries such as Hugging Face Transformers, OpenAI's API integrations, and other domain-specific tools is advantageous. Experience in large-scale deployment of ML projects, along with a good understanding of DevOps/MLOps/LLM Ops and training and fine-tuning of Large Language Models (SLMs) like PALM2, GPT4, LLAMA, etc., is beneficial. Key behavioral attributes for this role include the ability to mentor junior developers, take ownership of project deliverables, contribute to risk mitigation, and understand business objectives and functions to support data needs. If you have a Bachelor's or Master's degree in Computer Science, certifications in cloud technologies (AWS, Azure, GCP), and TOGAF certification (good to have), along with 11 to 14 years of relevant work experience, this role might be the perfect fit for you.,

You will be responsible for building curated enterprise-grade solutions for GenAI application deployment at a production scale for clients. This role demands a solid understanding and hands-on skills in GenAI application deployment, encompassing development and engineering skills. You will need to possess expertise in data ingestion, selecting the appropriate LLMs, implementing simple and advanced RAG, guardrails, prompt engineering for optimization, traceability, security, LLM evaluation, observability, and deployment at scale on cloud or on-premise. It is essential for candidates to demonstrate knowledge of agentic AI frameworks due to the rapid evolution of this space. Strong background in ML with engineering skills is highly preferred for the LLMOps role. You should have 3 - 5 years of experience working on ML projects, involving business requirement gathering, model development, training, deployment at scale, and monitoring model performance for production use cases. Proficiency in Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, AgentOps (optional) is crucial. Experience with proprietary and open-source large language models, LLM fine-tuning, creating distilled models from hosted LLMs, and building data pipelines for model training is required. You should also have experience in model performance tuning, RAG, guardrails, prompt engineering, evaluation, and observability. Prior experience in GenAI application deployment on cloud and on-premises at scale for production, creating CI/CD pipelines, working with Kubernetes, and deploying AI services on at least one cloud platform such as AWS, GCP, or Azure is necessary. Proficiency in creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen, and light-weight UI development using streamlit or chainlit (optional) is beneficial. Desired experience with open-source tools for ML development, deployment, observability, and integration is an added advantage. A background in DevOps and MLOps will be a plus. You should be familiar with collaborative code versioning tools like GitHub/GitLab and possess excellent communication and presentation skills. A degree in Computer Science, related technical field, or equivalent is required. If you are someone who thrives in a dynamic environment and enjoys collaborating with enthusiastic individuals, this opportunity is perfect for you.,

You will be responsible for leveraging GenAI models and frameworks to build applications and optimize AI solutions for clients. As the primary technical expert in GenAI technologies, you will focus on enhancing the GenAI footprint by developing and implementing advanced AI models. Your role will involve defining best practices, conducting workshops and demos, collaborating with cross-functional teams, and integrating emerging GenAI technologies for improving conversational AI capabilities. Additionally, you will provide technical leadership, mentorship, and guidance to teams, troubleshoot platform integration issues, and independently build prototypes to demonstrate to key decision-makers. To excel in this position, you should have at least 10 years of IT experience, with 3-4 years specifically in AI/Gen AI technologies such as OpenAI, Anthropic, or Hugging Face. Proficiency in NLP, NLU, NLQ, NLG technologies, and core Python expertise for building enterprise-scale applications are essential. You must have delivered production-grade projects on agentic AI frameworks, hands-on experience with frameworks like Autogen, Langraph, or Crew.AI, and expertise in NLQ to SQL-based GenAI applications using Python tools. Experience with cloud platforms like Microsoft Azure, AWS, or GCP, API integrations, and programming skills in Python, Node.js, or Java are also required. Moreover, you should possess strong problem-solving skills, solution architecture capabilities, and client-facing communication abilities to translate business requirements into technical solutions effectively. The ability to manage multiple projects in a fast-paced environment and experience working with cloud platforms will be beneficial. Additionally, expertise in building technical proposals, fast PoVs, and blueprints on new GenAI models and tools are desired for this role.,

We are looking for a skilled, motivated, and quick-learning Full Stack Developer to join our team dedicated to cutting-edge Gen AI development work. As a Full Stack Developer, you will be responsible for creating innovative applications and solutions that encompass both frontend and backend technologies. While our solutions often involve the use of Retrieval Augmented Generation (RAG) and Agentic frameworks, your role will extend beyond these technologies to encompass a variety of AI tools and techniques. Your responsibilities will include developing and maintaining web applications using Angular, NDBX frameworks, and other modern technologies. You will design and implement databases in Postgres DB, employ ingestion and retrieval pipelines utilizing pgvector and neo4j, and ensure the implementation of efficient and secure data practices. Additionally, you will work with various generative AI models and frameworks such as LangChain, Haystack, and LlamIndex for tasks like chucking, embeddings, chat completions, and integration with different data sources. You will collaborate with team members to integrate GenAI capabilities into applications, write clean and efficient code adhering to company standards, conduct testing to identify and fix bugs, and utilize collaboration tools like GitHub for effective team working and code management. Staying updated with emerging technologies and applying them to operations will be essential, showcasing a strong desire for continuous learning. Qualifications and Experience: - Bachelor's degree in Computer Science, Information Technology, or a related field with at least 6 years of working experience. - Proven experience as a Full Stack Developer with a focus on designing, developing, and deploying end-to-end applications. - Knowledge of front-end languages and libraries such as HTML/CSS, JavaScript, XML, and jQuery. - Experience with Angular and NDBX frameworks, as well as database technologies like Postgres DB and vector databases. - Proficiency in developing APIs following OpenAPI standards. - Familiarity with generative AI models on cloud platforms like Azure and AWS, including techniques like Retrieval Augmented Generation, Prompt engineering, Agentic RAG, and Model context protocols. - Experience with collaboration tools like GitHub and docker images for packaging applications. At Allianz, we believe in fostering a diverse and inclusive workforce. We are proud to be an equal opportunity employer that values bringing your authentic self to work, regardless of background, appearance, preferences, or beliefs. Together, we can create an environment where everyone feels empowered to explore, grow, and contribute to a better future for our customers and the global community. Join us at Allianz and let's work together to care for tomorrow.,

Job Position Title: Senior Associate_ APA Developer_Agentic Automation_Advisory_Bangalore Responsibilities: Design and develop agentic automation workflows using frameworks such as LangGraph, AutoGen, CrewAI, and other multi-agent systems (e.g., MCP, A2A) to automate complex business processes. Build and optimize Retrieval-Augmented Generation (RAG) pipelines for enhanced contextual understanding and accurate response generation in automation tasks. Integrate open-source LLMs (e.g. LLaMA) and closed-source LLMs (e.g., OpenAI, Gemini, Vertex AI) to power agentic systems and generative AI applications. Develop robust Python-based solutions using libraries like LangChain, Transformers, Pandas, and PyTorch for automation and AI model development. Implement and manage CI/CD pipelines, Git workflows, and software development best practices to ensure seamless deployment of automation solutions. Work with structured and unstructured data, applying prompt engineering and fine-tuning techniques to enhance LLM performance for specific use cases. Query and manage databases (e.g., SQL, NoSQL) for data extraction, transformation, and integration into automation workflows. Collaborate with stakeholders to translate technical solutions into business value, delivering clear presentations and documentation. Stay updated on advancements in agentic automation, generative AI, and LLM technologies to drive innovation and maintain competitive edge. Ensure scalability, security, and performance of deployed automation solutions in production environments. Experience: 4+ years of hands-on experience in AI/ML, generative AI, or automation development. Proven expertise in agentic frameworks like LangGraph, AutoGen, CrewAI, and multi-agent systems. Experience building and deploying RAG-based solutions for automation or knowledge-intensive applications. Hands-on experience with open-source LLMs (Hugging Face) and closed-source LLMs (OpenAI, Gemini, Vertex AI). Technical Skills: Advanced proficiency in Python and relevant libraries (LangChain, Transformers, Pandas, PyTorch, Scikit-learn). Strong SQL skills for querying and managing databases (e.g., PostgreSQL, MongoDB). Familiarity with CI/CD tools (e.g., Jenkins, GitHub Actions), Git workflows, and containerization (e.g., Docker, Kubernetes). Experience with Linux (Ubuntu) and cloud platforms (AWS, Azure, Google Cloud) for deploying automation solutions. Knowledge of automation tools (e.g., UiPath, Automation Anywhere) and workflow orchestration platforms. Soft Skills: Exceptional communication skills to articulate technical concepts to non-technical stakeholders. Strong problem-solving and analytical skills to address complex automation challenges. Ability to work collaboratively in a fast-paced, client-facing environment. Proactive mindset with a passion for adopting emerging technologies. Preferred Qualifications Experience with multi-agent coordination protocols (MCP) and agent-to-agent (A2A) communication systems. Familiarity with advanced generative AI techniques, such as prompt chaining, tool-augmented LLMs, and model distillation. Exposure to enterprise-grade automation platforms or intelligent process automation (IPA) solutions. Contributions to open-source AI/automation projects or publications in relevant domains. Certification in AI, cloud platforms, or automation technologies (e.g., AWS Certified AI Practitioner, RPA Developer). Mandatory skill sets: Agentic, LLM, RAG, AIML, LangGchain Preferred skill sets: Agentic, LLM, RAG, AIML, LangGchain, Gen AI Years of experience required: 4-7 Years

As an AI Azure Architect, your primary responsibility will be to develop the technical vision for AI systems that cater to the existing and future business requirements. This involves architecting end-to-end AI applications, ensuring seamless integration with legacy systems, enterprise data platforms, and microservices. Collaborating closely with business analysts and domain experts, you will translate business objectives into technical requirements and AI-driven solutions. Additionally, you will partner with product management to design agile project roadmaps aligning technical strategies with market needs. Coordinating with data engineering teams is essential to ensure smooth data flows, quality, and governance across different data sources. Your role will also involve leading the design of reference architectures, roadmaps, and best practices for AI applications. Evaluating emerging technologies and methodologies to recommend suitable innovations for integration into the organizational strategy is a crucial aspect of your responsibilities. You will be required to identify and define system components such as data ingestion pipelines, model training environments, CI/CD frameworks, and monitoring systems. Leveraging containerization (Docker, Kubernetes) and cloud services will streamline the deployment and scaling of AI systems. Implementation of robust versioning, rollback, and monitoring mechanisms to ensure system stability, reliability, and performance will be part of your duties. Moreover, you will oversee the planning, execution, and delivery of AI and ML applications, ensuring they are completed within budget and timeline constraints. Managing project goals, allocating resources, and mitigating risks will fall under your project management responsibilities. You will be responsible for overseeing the complete lifecycle of AI application developmentfrom conceptualization and design to development, testing, deployment, and post-production optimization. Emphasizing security best practices during each development phase, focusing on data privacy, user security, and risk mitigation, is crucial. In addition to technical skills, the ideal candidate for this role should possess key behavioral attributes such as the ability to mentor junior developers, take ownership of project deliverables, and contribute towards risk mitigation. Understanding business objectives and functions to support data needs is also essential. Mandatory technical skills for this position include a strong background in working with agents using langgraph, autogen, and CrewAI. Proficiency in Python, along with knowledge of machine learning libraries like TensorFlow, PyTorch, and Keras, is required. Experience with cloud computing platforms (AWS, Azure, Google Cloud Platform), containerization tools (Docker), orchestration frameworks (Kubernetes), and DevOps tools (Jenkins, GitLab CI/CD) is essential. Proficiency in SQL and NoSQL databases, designing distributed systems, RESTful APIs, GraphQL integrations, and event-driven architectures are also necessary. Preferred technical skills include experience with monitoring and logging tools, cutting-edge libraries like Hugging Face Transformers, and large-scale deployment of ML projects. Training and fine-tuning of Large Language Models (LLMs) is an added advantage. Educational qualifications for this role include a Bachelor's/Master's degree in Computer Science, along with certifications in Cloud technologies (AWS, Azure, GCP) and TOGAF certification. The ideal candidate should have 11 to 14 years of relevant work experience in this field.,

You will be responsible for building curated enterprise-grade solutions for GenAI application deployment at a production scale for clients. Your role will involve a solid understanding and hands-on skills for GenAI application deployment, which includes development and engineering tasks. This will include data ingestion, selecting suitable LLMs, implementing simple and advanced RAG, setting up guardrails, prompt engineering for optimization, ensuring traceability and security, evaluating LLMs, enabling observability, and deploying at scale on the cloud or on-premise. It is crucial that candidates also showcase knowledge on agentic AI frameworks, with a preference for those having a strong background in ML with engineering skills for the LLMOps role. The ideal candidate should possess 3 - 5 years of experience in working on ML projects, encompassing tasks such as business requirement gathering, model development, training, deployment at scale, and monitoring model performance for production use cases. Proficiency in Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, and optionally AgentOps is essential. Prior experience working with both proprietary and open-source large language models, fine-tuning LLMs, creating distilled models from hosted LLMs, building data pipelines for model training, and tuning model performance, RAG, guardrails, prompt engineering, evaluation, and observability is required. Moreover, candidates should have experience in GenAI application deployment on cloud and on-premises at scale for production, creating CI/CD pipelines, working with Kubernetes, deploying AI services on at least one cloud platform (AWS/GCP/Azure), creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen, and optionally developing lightweight UI using streamlit or chainlit. Desired experience with open-source tools for ML development, deployment, observability, and integration, as well as a background in DevOps and MLOps, will be advantageous. Proficiency in collaborative code versioning tools such as GitHub/GitLab, along with strong communication and presentation skills, is essential. A B.E/B.Tech/M.Tech in Computer Science or a related technical degree or equivalent qualification is required. If you are someone who enjoys challenging growth opportunities and thrives in a dynamic environment working alongside enthusiastic over-achievers, this role might be the perfect fit for you.,

At InnovationM , we're shaping the future of enterprise AI and are looking for a Senior Agentic AI Engineer to lead the design and development of cutting-edge agentic frameworks. Youll help build a scalable AI foundation that empowers multiple teams to deploy intelligent agents with autonomy, memory, and reasoning capabilities. Key Skills: Python | LangGraph | CrewAI | RAG | LLMs | Agentic Frameworks Tool Integration | Vector Databases | Autonomous Decision-Making Human-in-the-Loop | Secure, Scalable Architectures What Youll Do: Architect enterprise-grade AI frameworks Build reusable agentic patterns using LangGraph, CrewAI, AutoGPT Enable tools like APIs, vector stores, RPA, and internal services Collaborate across teams to bring autonomy into complex workflows Mentor and guide teams on the future of agentic AI Why Join Us? Work on the latest in AI/ML and agentic systems Engage with large-scale clients and impactful projects Thrive in a culture of innovation, learning & development Location: Noida Sec-126 l 5 Days work from Office Apply now: neha.sharma@innovationm.com

Job Title: AI Ops Engineer Experience: 35 years About the Role We are seeking a hands-on and proactive AI Ops Engineer to operationalize and support the deployment of large language model (LLM) workflows, including agentic AI applications, across Marvell’s enterprise ecosystem. This role requires strong prompt engineering capabilities, the ability to triage AI pipeline issues, and a deep understanding of how LLM-based agents interact with tools, memory, and APIs. You will be expected to diagnose and remediate real-time problems, from prompt quality issues to model behavior anomalies. Key Responsibilities Design, fine-tune, and manage prompts for various LLM use cases tailored to Marvell’s enterprise operations. Operate, monitor, and troubleshoot agentic AI applications , including identifying whether issues stem from: Prompt quality or structure Model configuration or performance Tool usage, API failures, or memory/recall issues Build diagnostics and playbooks to triage LLM-driven failures , including handling fallback strategies, retries, or re-routing to human workflows. Collaborate with architects, ML engineers, and DevOps to optimize agent orchestration across platforms like LangGraph, CrewAI, AutoGen, or similar. Support integration of agentic systems with enterprise apps like Jira, ServiceNow, Glean, or Confluence using REST APIs, webhooks, and adapters. Implement observability and logging best practices for model outputs, latency, and agent performance metrics. Contribute to building self-healing mechanisms and alerting strategies for production-grade AI workflows. Required Qualifications 3–6 years of experience in software engineering, DevOps, or ML Ops with exposure to AI/LLM workflows. Strong foundation in prompt engineering and experience with LLMs like GPT, Claude, LLaMA, etc. Practical understanding of AIOps platforms or operational AI use cases (incident triage, log summarization, root cause analysis, etc.). Exposure to agentic AI architectures , such as LangGraph, AutoGen, CrewAI, etc. Familiarity with scripting (Python), RESTful APIs, and basic system debugging. Strong analytical skills and the ability to trace issues across multi-step pipelines and asynchronous agents. Good-To-Have Glean DevRev Codium Cursor Atlassian AI Databricks Mosaic AI Role & responsibilities Preferred candidate profile

About Us We are a cutting-edge AI innovation company developing intelligent agents that transform the way businesses operate. Our mission is to push the boundaries of what's possible with large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration. We work with top-tier clients across industries to deliver agentic systems that solve real-world business problems with speed, scale, and intelligence. Role Overview Were looking for an AI Agent Engineer to design, build, and refine advanced autonomous agents that operate across complex environments. Youll work at the intersection of technical architecture, AI-driven solution consulting, and system designcollaborating closely with business leaders, developers, and product teams to create production-ready, intelligent systems. This is an ideal role for engineers who are passionate about the future of AI, thrive on experimentation, and want to shape the next generation of automation through LLMs and multi-agent workflows. Key Responsibilities Core Engineering Architect and implement end-to-end AI agents using LangGraph, AutoGen, CrewAI, and other multi-agent frameworks (e.g., MCP, A2A). Design, iterate, and optimize prompts to support reliable and accurate agent performance in business-critical use cases. Integrate leading LLMs (GPT-4, Claude, Gemini, open-source alternatives) into real-time workflows and internal systems. Implement RAG architectures using vector databases such as Qdrant, Chroma, or Weaviate to ground agent responses in relevant context. Connect agents to APIs, external tools, document stores, and operational platforms to support intelligent decision-making. Collaboration & Consulting Translate client and internal requirements into AI-first system designs and architecture. Consult with product managers, sales engineers, and data teams to align AI solutions with business priorities and feasibility. Support implementation through technical documentation, design reviews, and hands-on problem-solving. Innovation & Enablement Lead test-and-learn initiatives and proof-of-concepts to validate agent performance and business value. Stay current on rapid developments in the LLM and multi-agent ecosystem and drive adoption of new capabilities. Contribute to the internal AI platform with tools, patterns, and reusable components to accelerate development. Provide training and support to technical and non-technical stakeholders to drive adoption and governance. Qualifications Must-Have: 3+ years of experience in AI/ML engineering or intelligent system development. Strong Python programming skills, with hands-on experience in prompt engineering and LLM workflows. Experience with frameworks such as LangGraph, CrewAI, AutoGen, LangChain, or similar agent development tools. Proficiency in implementing RAG architectures and working with vector databases (e.g., Qdrant, Chroma, Weaviate). Integration experience with APIs, databases, and frontend or workflow tools. Demonstrated success in consulting, technical sales, or AI solution architecture. Awareness of AI safety, compliance, and responsible development practices. Nice-to-Have: Familiarity with orchestration tools like n8n, Replit, or low-code AI automation platforms. Experience in enterprise domains such as insurance, healthcare, legal tech, or customer service. Exposure to multi-modal systems (text + vision) or knowledge graphs. MLOps or AI infrastructure experience in cloud environments (AWS, Azure, GCP Youll Thrive In This Role If You: Are energized by building systems that operate autonomously and adaptively in real-world scenarios. Can quickly move from ideation to implementation with a test-and-learn mindset. Stay at the forefront of LLM advancements and understand how to apply them to business problems. Communicate effectively across disciplines and help bridge product, engineering, and customer value. Thrive in a fast-paced, experimental environment that balances deep technical rigor with user impact. Why Join Us Work on frontier problems in AI agent design and autonomous systems. Collaborate with top-tier clients and industry-leading experts. Flexible work culture built around autonomy, innovation, and continuous learning. Competitive compensation and opportunities for high-impact contributions.

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 - AI/ML Seeking an experienced GenAI/ML Engineer to integrate LLM APIs, build AI-driven applications, optimize model performance, and deploy AI services at scale. The ideal candidate has expertise in Python-based AI development, LLM orchestration, cloud deployment, and enterprise AI integration. Major focus should be at Gemni as CVS is GCP shop. Responsibilities . AI Application Development - Build and maintain Python-based AI services using LangChain, and CrewAI. Implement RAG-based retrieval and Agentic AI workflows. . LLM Integration & Optimization - Integrate Gemni, OpenAI, Azure OpenAI APIs. Optimize API calls using temperature, reduce hallucinations using embedding-based retrieval (FAISS, Pinecone). . Model Evaluation & Performance Tuning - Assess AI models using Model Scoring, fine-tune embeddings, and enhance similarity search for retrieval-augmented applications. . API & Microservices Development - Design scalable RESTful APIs services. Secure AI endpoints using OAuth2, JWT authentication, and API rate limiting. . Cloud Deployment & Orchestration - Deploy AI-powered applications using AWS Lambda, Kubernetes, Docker, CI/CD pipelines. Implement LangChain for AI workflow automation. . Agile Development & Innovation - Work in Scrum teams, estimate tasks accurately, and contribute to incremental AI feature releases. Qualifications we seek in you! Minimum Qualifications . BE /B.Tech/M.Tech/MCA . Experience in AI/ML: PyTorch, TensorFlow, Hugging Face, Pinecone . Experience in LLMs & APIs: OpenAI, LangChain, CrewAI . Experience in Cloud & DevOps: AWS, Azure, Kubernetes, Docker, CI/CD . Experience in Security & Compliance: OAuth2, JWT, HIPAA Preferred qualifications . Experience in AI/ML, LLM integrations, and enterprise cloud solutions . Proven expertise in GenAI API orchestration, prompt engineering, and embedding retrieval . Strong knowledge of scalable AI architectures and security best practices 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. For more information, visit www.genpact.com . Follow us on Twitter, 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.

Ready to build the future with AI At Genpact, we don&rsquot just keep up with technology&mdashwe set the pace. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what&rsquos possible, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Senior Manager - AI Architect We are seeking a highly skilled AI Architect to design, develop, and oversee artificial intelligence (AI) and agentic solutions. You will be responsible for creating scalable AI architectures, integrating AI models / agentic solutions into enterprise systems, and ensuring alignment with business objectives . This role requires deep technical expertise , strategic thinking, and the ability to work with cross-functional teams. Responsibilities: AI Strategy & Architecture: Design and implement AI/ML architectures that align with business goals. Define best practices for AI model lifecycle management , including training, deployment, and monitoring. Ensure A gentic solutions are scalable, secure, and efficient for enterprise deployment. AI Development & Integration: Select the right AI frameworks, tools, and platforms for business needs. Oversee the development and integration of AI models into enterprise applications. Collaborate with data scientists and engineers to optimize AI models for performance and accuracy. Governance & Compliance: Establish AI governance frameworks to ensure ethical and responsible AI usage. Ensure compliance with data privacy laws (e.g., GDPR, CCPA) and industry regulations. Implement robust AI security measures to protect against adversarial attacks. Collaboration & Leadership: Work closely with business leaders, IT teams, and data science teams to translate requirements into AI solutions. Lead and mentor AI developers Stay updated with the latest AI trends and drive innovation in AI adoption within the organization. Qualifications we seek in you! Minimum qualifications: Technical Skills: Expertise in machine learning, deep learning, and NLP (TensorFlow, PyTorch , Hugging Face, etc.). Fine-tuning and Prompt Engineering - Optimizing LLMs like GPT-4, LLaMA , Claude, and Gemini for agentic behavior. Retrieval-Augmented Generation (RAG) - Integrating LLMs with knowledge bases for real-time decision-making. Agentic Frameworks - Proficiency in LangChain , AutoGPT , BabyAGI , or CrewAI for building autonomous agents. Strong knowledge of cloud AI platforms (AWS, Azure AI, Google Vertex AI). Proficiency in programming languages (Python, Java, R, etc.). Understanding of MLOps practices for model deployment and monitoring. Experience with API development and microservices architecture. Soft Skills: Strong analytical and problem-solving skills. Excellent communication and stakeholder management abilities. Ability to work in a fast-paced, cross-functional environment. Preferred Qualifications: Master&rsquos in Computer Science , AI, Data Science, or related fields. Certifications in AI/ML (e.g., AWS Certified Machine Learning, Google Professional ML Engineer). Relevant years of experience in software development or equivalent Experience in AI ethics and bias mitigation (Optional) Why join Genpact Lead AI-first transformation - Build and scale AI solutions that redefine industries Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career &mdashGain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills Grow with the best - Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace Committed to ethical AI - Work in an environment where governance, transparency, and security are at the core of everything we build Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the 140,000+ coders, tech shapers, and growth makers at Genpact and take your career in the only direction that matters: Up . Let&rsquos build tomorrow together. 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. 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.

Required Qualifications: 3 - 5 years of experience in working on ML projects that includes business requirement gathering, model development, training, deployment at scale and monitoring model performance for production use cases Strong knowledge on Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, AgentOps (optional) Should have worked on proprietary and open-source large language models Experience on LLM fine tuning, creating distilled model from hosted LLMs Building data pipelines for model training Experience on model performance tuning, RAG, guardrails, prompt engineering, evaluation,and observability Experience in GenAI application deployment on cloud and on-premises at scale for production Experience in creating CI/CD pipelines Working knowledge on Kubernetes Experience in minimum one cloud: AWS / GCP / Azure to deploy AI services Experience in creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen Experience in light weight UI development using streamlit or chainlit (optional) Desired experience onopen-source toolsfor ML development, deployment, observability, and integration Background on DevOps and MLOps will be a plus Experience working on collaborative code versioning tools like GitHub/GitLab Team player with good communication and presentation skills EDUCATION: B.E/B.Tech/M.Tech in Computer Science or related technical degree OR Equivalent.

Required Qualifications: 3 - 5 years of experience in working on ML projects that includes business requirement gathering, model development, training, deployment at scale and monitoring model performance for production use cases Strong knowledge on Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, AgentOps (optional) Should have worked on proprietary and open-source large language models Experience on LLM fine tuning, creating distilled model from hosted LLMs Building data pipelines for model training Experience on model performance tuning, RAG, guardrails, prompt engineering, evaluation,and observability Experience in GenAI application deployment on cloud and on-premises at scale for production Experience in creating CI/CD pipelines Working knowledge on Kubernetes Experience in minimum one cloud: AWS / GCP / Azure to deploy AI services Experience in creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen Experience in light weight UI development using streamlit or chainlit (optional) Desired experience onopen-source toolsfor ML development, deployment, observability, and integration Background on DevOps and MLOps will be a plus Experience working on collaborative code versioning tools like GitHub/GitLab Team player with good communication and presentation skills EDUCATION: B.E/B.Tech/M.Tech in Computer Science or related technical degree OR Equivalent.

Required Qualifications: 3 - 5 years of experience in working on ML projects that includes business requirement gathering, model development, training, deployment at scale and monitoring model performance for production use cases Strong knowledge on Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, AgentOps (optional) Should have worked on proprietary and open-source large language models Experience on LLM fine tuning, creating distilled model from hosted LLMs Building data pipelines for model training Experience on model performance tuning, RAG, guardrails, prompt engineering, evaluation,and observability Experience in GenAI application deployment on cloud and on-premises at scale for production Experience in creating CI/CD pipelines Working knowledge on Kubernetes Experience in minimum one cloud: AWS / GCP / Azure to deploy AI services Experience in creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen Experience in light weight UI development using streamlit or chainlit (optional) Desired experience onopen-source toolsfor ML development, deployment, observability, and integration Background on DevOps and MLOps will be a plus Experience working on collaborative code versioning tools like GitHub/GitLab Team player with good communication and presentation skills EDUCATION: B.E/B.Tech/M.Tech in Computer Science or related technical degree OR Equivalent.

Title- AI Engineer Experience - 4 to 6 Years Location - Noida Sec 126 Note-We are looking for a candidate who can Join within July and or having maximum 1 month of Notice period Skills Python Agentic Frameworks AI Models LangGraph Agentic workflows Vector Database AI/ML LLM RAG CrewAI Project Highlights We are seeking a Senior Agentic AI Engineer to lead the design and development of an enterprise-grade Agentic AI framework. This framework will empower various business units across the organization to build intelligent, autonomous agents capable of handling complex workflows, using tools, interacting with APIs, and making decisions with minimal human intervention.This role requires deep expertise in architecting scalable, modular AI systems using LLMs, tool integration, memory systems, and agent orchestration. This role is ideal for someone who has hands-on experience with agentic AI frameworks like LangGraph, CrewAI and AutoGPT and a deep understanding of enterprise software engineering and system architecture. Roles and Responsibilites Architect and build a reusable, secure, and scalable Agentic AI framework that can be adopted by multiple teams across the enterprise. Define standards, patterns, and abstractions for building intelligent agents using LLMs and other foundational models. Leverage and extend frameworks like LangGraph, CrewAI, or Semantic Kernel to enable advanced agentic capabilities such as (Long-horizon task planning, Tool calling (APIs, databases, RPA, internal services), Memory persistence and retrieval (via vector stores or knowledge graphs),Autonomous decision-making and reflection,Multi-agent orchestration and collaboration, Human-in-the-loop workflows,Monitoring and observability,Governance and compliance,Security and access control,Performance optimization and cost management Collaborate with cross-functional teams to ensure the framework aligns with enterprise-grade expectations. Integrate the framework with existing enterprise platforms. Conduct research and stay updated on the latest advancements in Agentic AI, LLMs, and related technologies. Mentor and guide junior engineers in best practices for building agentic systems. Coach and guide engineering teams in adopting and extending the framework. Requirements 4 -6 years of software engineering or AI/ML experience, with 2+ years in agentic or LLM-based system design. Hands-on experience with LangGraph, CrewAI, AutoGPT, BabyAGI, or similar frameworks. Proficiency in Python and common AI/LLM libraries (LangChain, OpenAI API, Hugging Face, etc.). Strong grasp of agent lifecycle, orchestration, and autonomy patterns (e.g., ReAct, Plan-and-Execute, hierarchical agents). Experience in integrating LLM agents with tools (APIs, search, file systems, databases) and managing secure, compliant interaction models. Solid understanding of vector stores , memory architectures, and context management strategies. Experience deploying agentic frameworks in large-scale enterprise environments (finance, telecom, healthcare, etc.) and Knowledge of LangGraph's stateful workflows will be a plus. Benefits Of Working With Us Opportunities to work in Latest Technologies. Challenging / Complex Development Projects. Working with Large clients. Strong Development & Delivery Processes. Focus on Learning and Development. Send your profile to: nikita.gautam@innovationm.com or you can connect over linkedin:- https://www.linkedin.com/in/gautamnikita/

Responsibilities: * Design, develop, test & maintain software solutions using Python, React, AWS, Kubernetes, LLMs, Langchain & Crewai. * Collaborate with cross-functional teams on project delivery & technical strategy.

Must Have Good Experience With GenAI + Autogen + CrewAI + WrenAI Hiring for Technical Architect Role Company have offices in all the mentioned Locations For the Quickest Response Whatsapp on 8287377768 Ayush ( Don't call )

The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands-on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front-end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions. Key Responsibilities: Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications. OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools. API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process. RAG and Modular RAG : Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance. Data Curation Automation : Build tools and pipelines for automated data curation and preprocessing. Technical Documentation : Create detailed technical documentation for developed applications and processes. Collaboration : Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship : Guide and mentor junior developers, fostering a culture of technical excellence and innovation. Required Skills : Python Programming : Deep expertise in Python for building GenAI applications and automation tools. Productionization of GenAI application beyond PoCs Using scale frameworks and tools such as Pylint,Pyritetc. LLM Frameworks : Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Front-End Technologies : Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development. Cloud Platforms : Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning Techniques : Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps : Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI : Expertise in implementing ethical AI practices and ensuring compliance with regulations. RAG and Modular RAG : Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures. Data Modernization : Expertise in modernizing and transforming data for GenAI applications. OCR and Document Intelligence : Proficiency in OCR and document intelligence using cloud-based tools. API Integration : Experience with REST, SOAP, and other protocols for API integration. Data Curation : Expertise in building automated data curation and preprocessing pipelines. Technical Documentation : Ability to create clear and comprehensive technical documentation. Collaboration and Communication : Strong collaboration and communication skills to work effectively with cross-functional teams. Mentorship : Proven ability to mentor junior developers and foster a culture of technical excellence.

The Implementation Technical Architect will be responsible for designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as fine-tuning, LLMOps, and Responsible AI. The architect will lead the development of scalable, secure, and efficient GenAI applications, ensuring alignment with business goals and technical requirements. Key Responsibilities: Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code. Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning. Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure. Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases. LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications. Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability. RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance. Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications. Open-Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment. Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces. SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects. Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects. Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship and Leadership: Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence. Required Skills: Python Programming: Deep expertise in Python for building GenAI applications and automation tools. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation. Modular RAG: Deep understanding of Modular RAG architectures and their implementation. Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development. Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities. SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs. Data Curation: Expertise in building automated data curation and preprocessing pipelines. API Development: Experience in designing and implementing APIs for GenAI applications. Technical Documentation: Ability to create clear and comprehensive design artifacts and technical documentation. Leadership and Mentorship: Proven ability to lead teams, mentor junior developers, and drive technical innovation.

The Implementation Technical Architect will be responsible for designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as fine-tuning, LLMOps, and Responsible AI. The architect will lead the development of scalable, secure, and efficient GenAI applications, ensuring alignment with business goals and technical requirements. Key Responsibilities: Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code. Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning. Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure. Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases. LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications. Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability. RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance. Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications. Open-Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment. Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces. SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects. Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects. Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship and Leadership: Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence. Required Skills: Python Programming: Deep expertise in Python for building GenAI applications and automation tools. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation. Modular RAG: Deep understanding of Modular RAG architectures and their implementation. Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development. Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities. SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs. Data Curation: Expertise in building automated data curation and preprocessing pipelines. API Development: Experience in designing and implementing APIs for GenAI applications. Technical Documentation: Ability to create clear and comprehensive design artifacts and technical documentation. Leadership and Mentorship: Proven ability to lead teams, mentor junior developers, and drive technical innovation.