15 Ai Engineering Jobs

As an AI Engineer at Tiger Analytics in the Data and AI team, you will have the opportunity to work on internal strategic initiatives as well as tackle complex AI/ML challenges for clients. Your role will involve collaborating closely with senior leadership, including one of the founders, to develop an innovative enterprise platform. This platform will serve as a digital twin of existing systems, functioning as an intelligent bot/platform that offers real-time insights and guidance on tasks. If you have a passion for Generative AI, LLMs, and cutting-edge technologies, and enjoy creating high-impact solutions, we invite you to join our team. Your responsibilities will include: - Working with leadership to grasp the vision, scope, and requirements of the enterprise platform. - Designing and constructing a smart, AI-driven platform that optimizes projects with real-time intelligence. - Utilizing advanced technologies like LLMs (Large Language Models) and Generative AI to develop transformative tools. - Integrating the platform seamlessly with existing organizational systems to ensure smooth operations. - Contributing to the development of scalable, reliable, and high-performance software solutions. - Engaging in peer reviews and promoting code quality within the team. The ideal candidate for this role should possess: - 2-6 years of experience in MLE/MLOps/AI Engineering. - Previous exposure to LLMs, Generative AI, or similar AI technologies. - Proficiency in backend development using Python (Django/Flask) or equivalent technologies. - A solid understanding of APIs and microservices architecture. - The ability to collaborate effectively with cross-functional teams in a dynamic environment. - A keen interest in learning and leveraging new technologies to address intricate challenges. At Tiger Analytics, we value diversity and inclusivity. We encourage individuals with varying skill sets and qualities to apply, even if they do not meet all the criteria for the role. We are committed to being an equal opportunity employer, fostering a culture where listening, trust, respect, and personal growth are prioritized. Please note that the job title and compensation will be determined based on your expertise and experience level. Additionally, our competitive compensation packages are designed to be among the best in the industry. In addition to your salary, we offer a range of benefits including health insurance for yourself and your family, access to a virtual wellness platform, and opportunities to engage with various knowledge communities.,

Job Description As a Python Backend Engineer with exposure to AI engineering at Quantanite, you will be an integral part of our team responsible for building a scalable, cognitive data platform. Your role will involve designing and developing high-performance backend services using Python (FastAPI), developing RESTful APIs for data ingestion, transformation, and AI-based feature access, and collaborating closely with DevOps and data engineering teams to integrate backend services with Azure data pipelines and databases. Your primary responsibilities will include managing database schemas, writing complex SQL queries, and supporting ETL processes using Python-based tools. Additionally, you will be tasked with building secure, scalable, and production-ready services that adhere to best practices in logging, authentication, and observability. You will also implement background tasks and async event-driven workflows for data crawling and processing. In terms of AI engineering contributions, you will support the integration of AI models (NLP, summarization, information retrieval) within backend APIs. You will collaborate with the AI team to deploy lightweight inference pipelines using PyTorch, TensorFlow, or ONNX, and participate in training data pipeline design and minor model fine-tuning as needed for business logic. Furthermore, you will contribute to the testing, logging, and monitoring of AI agent behavior in production environments. To be successful in this role, you should have at least 3 years of experience in Python backend development, with a strong proficiency in FastAPI or equivalent frameworks. A solid understanding of RESTful API design, asynchronous programming, and web application architecture is essential. Additionally, you should demonstrate proficiency in working with relational databases (e.g., PostgreSQL, MS SQL Server) and Azure cloud services, as well as experience with ETL workflows, job scheduling, and data pipeline orchestration (Airflow, Prefect, etc.). Exposure to machine learning libraries (e.g., Scikit-learn, Transformers, OpenAI APIs) is a plus, along with familiarity with containerization (Docker), CI/CD practices, and performance tuning. A mindset of code quality, scalability, documentation, and collaboration is highly valued at Quantanite. If you are looking for a challenging yet rewarding opportunity to work in a collaborative environment with a focus on innovation and growth, we encourage you to apply to join our dynamic team at Quantanite.,

The Head of R&D AI Engineering at Sanofi is a crucial role within the R&D Data & AI Products and Platforms Team, where you will play a pivotal part in driving the development and delivery of Data and AI products to enhance R&D operations. As part of the digital transformation program at Sanofi, you will lead the acceleration of data transformation and adoption of artificial intelligence (AI) and machine learning (ML) solutions to improve R&D, manufacturing, and commercial performance. Your main responsibilities will include developing and executing a vision and strategy for AI/ML product development, leading AI product development in alignment with R&D data strategy and business goals, and ensuring the deployment of AI/ML models through APIs/endpoints. You will collaborate closely with AI/ML model development teams, manage project timelines and resource allocation, and promote bias protection and fairness within AI/ML technologies. As the Head of R&D AI Engineering, you will also be responsible for building and leading a high-performing team of engineers, providing mentorship, training, and resources to empower the team to deliver engineering excellence. You will work towards continuous improvement in AI/ML product delivery and provide guidance and support to overcome any challenges faced by AI Engineers during product delivery. Furthermore, you will collaborate with Platform and Data Engineering Teams to maintain data pipelines and consistent ways of working across teams, and partner with Enterprise Digital to oversee AI/ML data protection, governance, and compliance. Your expertise in machine learning, deep learning, natural language processing, and other related ML/AI technologies, along with experience in programming languages such as Python, R, Java, and cloud platforms, will be essential in this role. Sanofi is committed to providing a supportive and future-focused environment where you can bring the miracles of science to life. With numerous opportunities for growth and career development, a well-crafted rewards package, and a wide range of health and wellbeing benefits, Sanofi offers an empowering and rewarding career journey. Join us in pursuing progress and discovering extraordinary possibilities in the field of science and healthcare.,

The AI Presales Consultant is responsible for bridging the gap between technical expertise in Artificial Intelligence (AI) and the business requirements of international prospects. You will engage directly with clients to identify industry-specific use cases for Generative AI, design tailored solutions, collaborate with AI engineering teams, and develop compelling proposals that clearly articulate business value and technical feasibility. Your key responsibilities will include conducting industry-specific research to identify relevant AI and Generative AI use cases, assessing and differentiating between viable solutions, collaborating with AI Engineers to design tailored AI solutions, developing detailed user flows and solution architecture documents, leading client-facing demonstrations and proof-of-concept presentations, and maintaining strong client relationships. To qualify for this role, you should have a Bachelor's Degree in Computer Science, Engineering, IT, or related field, with advanced degrees preferred. You should have at least 7 years of experience in AI presales, solutions consulting, or business analysis roles. Demonstrable understanding of core AI and Generative AI concepts, experience in creating technical proposals, excellent communication and presentation skills, and the ability to manage multiple client engagements concurrently are essential qualifications. Preferred attributes include experience working with global clients, a self-motivated approach to learning emerging technologies, strong organizational skills, and familiarity with AI tools and cloud platforms. If you have a passion for innovation and possess the required technical expertise and business acumen, this role offers an exciting opportunity to drive strategic business enablement through AI solutions.,

What will you do Voice AI Stack Ownership: Build and own the end-to-end voice bot pipeline ASR, NLU, dialog state management, tool calling, and TTS to create a natural, human-like conversation experience. LLM Orchestration & Tooling: Architect systems using MCP (Model Context Protocol) to mediate structured context between real-time ASR, memory, APIs, and the LLM. RAG Integration: Implement retrieval-augmented generation to ground responses using dealership knowledge bases, inventory data, recall lookups, and FAQs. Vector Store & Memory: Design scalable vector-based search for dynamic FAQ handling, call recall, and user-specific memory embedding. Latency Optimization: Engineer low-latency, streaming ASR + TTS pipelines and fine-tune turn-taking models for natural conversation. Model Tuning & Hallucination Control: Use fine-tuning, LoRA, or instruction tuning to customize tone, reduce hallucinations, and align responses to business goals. Instrumentation & QA Looping: Build robust observability, run real-time call QA pipelines, and analyze interruptions, hallucinations, and fallbacks. Cross-functional Collaboration: Work closely with product, infra, and leadership to scale this bot to thousands of US dealerships. What will make you successful in this role Architect-level thinking: You understand how ASR, LLMs, memory, and tools fit together and can design modular, observable, and resilient systems. LLM Tooling Mastery: You've implemented tool calling, retrieval pipelines, function calls, or prompt chaining across multiple workflows. Fluency in Vector Search & RAG: You know how to chunk, embed, index, and retrieve and how to avoid prompt bloat and token overflow. Latency-First Mindset: You debug token delays, know the cost of each API hop, and can optimize round-trip time to keep calls human-like. Grounding > Hallucination: You know how to trace hallucinations back to weak prompts, missing guardrails, or lack of tool access and fix them. Prototyper at heart: You're not scared of building from scratch and iterating fast, using open-source or hosted tools as needed. What you must have 5+ years in AI/ML or voice/NLP systems with real-time experience Deep knowledge of LLM orchestration, RAG, vector search, and prompt engineering Experience with MCP-style architectures or structured context pipelines between LLMs and APIs/tools Experience integrating ASR (Whisper/Deepgram), TTS (ElevenLabs/Coqui), and OpenAI/GPT-style models Solid understanding of latency optimization, streaming inference, and real-time audio pipelines Hands-on with Python, FastAPI, vector DBs (Pinecone, Weaviate, FAISS), and cloud infra (AWS/GCP) Strong debugging, logging, and QA instincts for hallucination, grounding, and UX behavior

Build and own the full voice bot pipeline including ASR, NLU, dialog management, tool calling, and TTS. Architect systems using MCP to connect ASR, memory, APIs, and LLMs in real-time. Implement RAG to ground responses using data from knowledge bases, inventory, and FAQs. Design scalable vector search systems for memory embedding and FAQ handling. Engineer low-latency ASR and TTS pipelines, optimizing for natural turn-taking. Apply fine-tuning, LoRA, and instruction tuning to reduce hallucinations and align model tone. Build observability systems and QA pipelines to monitor calls and analyze model behavior. Collaborate with cross-functional teams to scale the voice bot to thousands of users. Design modular, observable, and resilient AI systems. Implement retrieval pipelines, function calls, and prompt chaining across workflows. Expertly chunk, embed, and retrieve documents in RAG systems. Debug latency issues and optimize for low round-trip time. Trace hallucinations to root causes and fix via guardrails or tool access. Build prototypes using open-source or hosted tools with speed and flexibility. 5+ years in AI/ML or voice/NLP with real-time experience. Deep knowledge of LLM orchestration, vector search, and prompt engineering. Experience with ASR (Whisper, Deepgram), TTS (ElevenLabs, Coqui), and OpenAI models. Skilled in latency optimization and real-time audio pipelines. Hands-on with Python, FastAPI, vector DBs, and cloud platforms.

What You ll Do Design & Build: Develop mutli-agent AI systems for the UCaaS platform, focusing on NLP, speech recognition, audio intelligence and LLM powered interactions. Rapid Experiments: Prototype with open-weight models (Mistral, LLaMA, Whisper, etc.) and scale what works. Code for Excellence: Write robust code for AI/ML libraries and champion software best practices. Optimize for Scale & Cost: Engineer scalable AI pipelines, focusing on latency, throughput, and cloud costs. Innovate with LLMs: Fine-tune and deploy LLMs for summarization, sentiment and intent detection, RAG pipelines, multi-modal inputs and multi-agentic task automation. Own the Stack: Lead multi-agentic environments from data to deployment and scale. Collaborate & Lead: Integrate AI with cross-functional teams and mentor junior engineers. What You Bring Experience:6-10 yearsof professional experience, with a mandatory minimum of 2 years dedicated to a hands-on role in a real-world, production-level AI/ML project. Coding & Design: Expert-level programming skills inPythonand proficiency in designing and building scalable, distributed systems. ML/AI Expertise: Deep, hands-on experience with coreML/AI libraries and frameworks, Agentic Systems, RAG pipelines Hands-on experience in usingVector DBs LLM Proficiency: Proven experience working with and fine-tuning Large Language Models (LLMs). Scalability & Optimization Mindset: Demonstrated experience in building and scaling AI services in the cloud, with a strong focus on performance tuning and cost optimization of agents specifically. Nice to Have Youve tried outagent frameworkslike LangGraph, CrewAI, or AutoGen and can explain the pros and cons of autonomous vs. orchestrated agents. Experience with MLOps tools and platforms (e.g., Kubeflow, MLflow, Sagemaker). Real-time streaming AI experience token-level generation, WebRTC integration, or live transcription systems Contributions to open-source AI/ML projects or a strong public portfolio (GitHub, Kaggle).

We are looking for exceptional individuals to join our team at ScalePad as Head of AI Engineering. ScalePad is a prominent software-as-a-service (SaaS) company operating globally to provide Managed Service Providers (MSPs) with the tools and support needed to enhance client value in the ever-evolving IT landscape. As a member of our tech-management team, you will lead AI development initiatives, shape our AI strategy, and guide teams in creating impactful AI applications. This hands-on leadership role involves mentoring teams, improving developer productivity, and ensuring best practices in AI development, software engineering, and system design. Your responsibilities will include designing state-of-the-art AI applications, leveraging advanced techniques such as Machine Learning (ML), Large Language Models (LLMs), Graph Neural Networks (GNNs), and Retrieval-Augmented Generation (RAG). You will also focus on fostering an environment of responsible AI practices, governance, and ethics, advocating for AI-first product thinking, and collaborating with various teams to align AI solutions with business objectives. To excel in this role, you should possess strong technical expertise in AI, ML, software architecture principles, and have a proven track record of integrating AI advancements into engineering execution. Additionally, experience in AI governance, ethics, and managing globally distributed teams will be essential. We are seeking a curious, hands-on leader who is passionate about developing talent, driving innovation, and ensuring AI excellence within our organization. Joining our team at ScalePad will offer you the opportunity to lead the evolution of AI-driven products, work with cutting-edge technologies, and make a global impact by influencing AI-powered decision-making at an enterprise level. As a Rocketeer, you will enjoy ownership through our Employee Stock Option Plan (ESOP), benefit from annual training and development opportunities, and work in a dynamic, entrepreneurial setting that promotes growth and stability. If you are ready to contribute to a culture of innovation, collaboration, and success, we invite you to apply for this role. Please note that only candidates eligible to work in Canada will be considered. At ScalePad, we are committed to fostering Diversity, Equity, Inclusion, and Belonging (DEIB) to create a workplace where every individual's unique experiences and perspectives are valued. Join us in building a stronger, more inclusive future where everyone has the opportunity to thrive and grow.,

3+ Years experience in Gen AI, AI Engineering, Solution Architecture Gen AI Specialist strong experience in LLM, AI engineering, AI project deployment & production, Gen AI innovation, Gen AI architecture, Gen AI based use case design, build, deploy & scale at clients. 2 years + Strong experience in AI, ML, Data Science. Solutioning, Architecture, Build, Deploy, ML Ops, Production, Managed Services Top Engineering Colleges Preferred Prefer is resource is from tech startup. Consulting and Tech Firms are also good. Cloud Firms too if we can afford Full Stack Tech Skills i.e. Front End, Middle End, Back End, Custom Apps, Automation will be a big bonus Experience in Solution Architecture for projects, AI Project Delivery hands on,

:Mandatory Skills: 1. 5+ Years of experience in the design & development of state-of-the-art language models; utilize off-the-shelf LLM services, such as Azure OpenAI, to integrate LLM capabilities into applications. 2. Deep understanding of language models and a strong proficiency in designing and implementing RAG-based workflows to enhance content generation and information retrieval. 3. Experience in building, customizing and fine-tuning LLMs via OpenAI studio extended through Azure OpenAI cognitive services for rapid PoCs 4. Proven track record of successfully deploying and optimizing LLM models in the cloud (AWS, Azure, or GCP) for inference in production environments and proven ability to optimize LLM models for inference speed, memory efficiency, and resource utilization. 5. Apply prompt engineering techniques to design refined and contextually relevant prompts for language models. 6. Monitor and analyze the performance of LLMs by experimenting with various prompts, evaluating results, and refining strategies accordingly. 7. Building customizable, conversable AI agents for complex tasks using CrewAI and LangGraph to enhance Gen AI solutions 8. Proficient in MCP (Model Context Protocol) for optimizing context-aware AI model performance and integration is a plus Location - Mumbai , Pune, Bangalore, Chennai and Noida

We seek a Senior AI Scientist with strong ML fundamentals and data engineering expertise to lead the development of scalable AI/LLM solutions. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices. Key Responsibilities 1. AI/LLM Development: o Fine-tune and optimize LLMs (e.g., GPT, Llama) and traditional ML models for production. o Implement retrieval-augmented generation (RAG), vector databases, and orchestration tools (e.g., LangChain). 2. Data Engineering: o Build scalable data pipelines for unstructured/text data (e.g., Spark, Kafka, Airflow). o Optimize storage/retrieval for embeddings (e.g., pgvector, Pinecone). 3. MLOps & Deployment: o Containerize models (Docker) and deploy on cloud (AWS/Azure/GCP) using Kubernetes. o Design CI/CD pipelines for LLM workflows (experiment tracking, monitoring). 4. Collaboration: o Work with DevOps to optimize latency/cost trade-offs for LLM APIs. o Mentor junior team members on ML engineering best practices. Required Skills & Qualifications Education: MS/PhD in CS/AI/Data Science (or equivalent experience). Experience: 6+ years in ML + data engineering, with 2+ years in LLM/GenAI projects.

Job Summary: We are seeking a versatile QA & AIOps Engineer with a minimum of 5 years of hands-on experience in both AI Ops implementation and intelligent QA automation. The ideal candidate will bridge the gap between DevOps, QA, and AI engineering ensuring robust infrastructure, AI agent deployment, intelligent testing, and automated monitoring for scalable AI systems. This role requires strong technical depth in cloud-native deployment (AKS/EKS), CI/CD pipelines, and AI/ML toolchains. Key Responsibilities: AI Ops & DevOps Engineering: Deploy and manage AI agents in scalable production environments (Azure/AWS). Set up and maintain Kubernetes clusters (AKS/EKS), Docker containers, and Ingress Controllers (NGINX/Traefik). Integrate platforms like Azure AI Foundry or AWS Bedrock into production pipelines. Implement auto-scaling policies using tools like KEDA or Horizontal Pod Autoscaler. Promote use of Vector Databases (e.g., Pinecone, Weaviate) for AI workflows. Configure infrastructure security: secrets management, access controls, and network policies. Use Helm charts to manage complex deployments and upgrades of AI services. Troubleshoot production issues, identify bottlenecks, and ensure high availability of AI workloads. Collaborate with SREs, DevOps, and ML engineers to ensure seamless agent deployment, monitoring, and scaling. QA & Intelligent Testing: Design, develop, and execute test cases (manual and automated) for web, API, and backend systems. Implement AI-driven test strategies (e.g., test prioritization, risk-based testing). Create and maintain automated test scripts using tools like JUnit, PyTest, and JMeter. Integrate QA automation into CI/CD pipelines with anomaly detection and predictive analytics. Log and manage bugs; track issues to closure with development and DevOps teams. Use AI-powered QA tools such as Test.ai, Functionize for test optimization. Monitor AIOps dashboards to validate intelligent issue detection and auto-remediation flows. Required Skills & Qualifications: 5+ years of combined experience in QA automation, DevOps, or AI Ops roles. Hands-on expertise with Kubernetes (AKS/EKS), Docker, Helm, CI/CD pipelines. Experience integrating AI/ML tools such as Azure AI Foundry, AWS Bedrock. Knowledge of AI Ops platforms, observability tools, and monitoring dashboards. Proficient in Python, Bash, or JavaScript. Familiarity with Vector DBs, AI observability, and security practices. Strong collaboration skills with cross-functional DevOps, QA, and AI engineering teams.

Job Summary: We are seeking a passionate and skilled AI Engineer to design, develop, and deploy cutting-edge AI solutions across domains such as large language models (LLMs), computer vision, and autonomous agent workflows. You will collaborate with data scientists, researchers, and engineering teams to build intelligent systems that solve real-world problems using deep learning, transformer-based architectures, and multi-modal AI models. Key Responsibilities: Design and implement AI/ML models, especially transformer-based LLMs (e.g., BERT, GPT, LLaMA) and vision models (e.g., ViT, YOLO, Detectron2). Develop and deploy computer vision pipelines for object detection, segmentation, OCR, and image classification tasks. Build and orchestrate intelligent agent workflows using prompt engineering, memory systems, retrieval-augmented generation (RAG), and multi-agent coordination. Fine-tune and optimize pre-trained models on domain-specific datasets using frameworks like PyTorch or TensorFlow. Collaborate with cross-functional teams to understand problem requirements and translate them into scalable AI solutions. Implement inference pipelines and APIs to serve AI models efficiently using tools such as FastAPI, ONNX, or Triton Inference Server. Conduct model evaluation, benchmarking, A/B testing, and performance tuning. Stay updated with state-of-the-art research in deep learning, generative AI, and multi-modal learning. Ensure reproducibility, versioning, and documentation of all experiments and production models. Qualifications: Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 35 years of hands-on experience in designing and deploying deep learning models. Strong knowledge of LLMs (e.g., GPT, BERT, T5), Vision Models (e.g., CNNs, Vision Transformers), and Computer Vision techniques. Experience building intelligent agents or using frameworks like LangChain, Haystack, AutoGPT, or similar. Proficiency in Python, with expertise in libraries such as PyTorch, TensorFlow, Hugging Face Transformers, OpenCV, and Scikit-learn. Familiarity with MLOps concepts and deployment tools (Docker, Kubernetes, MLflow). Strong understanding of NLP, image processing, model fine-tuning, and optimization. Experience with cloud platforms (AWS, GCP, Azure) and GPU environments. Excellent problem-solving, communication, and teamwork skills. Preferred Qualifications: Experience in building multi-modal AI systems (e.g., combining vision + language models). Exposure to real-time inference systems and low-latency model deployment. Contributions to open-source AI projects or research publications. Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and RAG pipelines. Locations : Mumbai, Delhi / NCR, Bengaluru , Kolkata, Chennai, Hyderabad, Ahmedabad, Pune, India

Seniority : Senior Description & Requirements Position Summary The Senior AI Engineer with GenAI expertise is responsible for developing advanced technical solutions, integrating cutting-edge generative AI technologies. This role requires a deep understanding of modern technical and cloud-native practices, AI, DevOps, and machine learning technologies, particularly in generative models. You will support a wide range of customers through the Ideation to MVP journey, showcasing leadership and decision-making abilities while tackling complex challenges. Key Responsibilities Technical & Engineering Leadership Develop solutions leveraging GenAI technologies, integrating advanced AI capabilities into cloud-native architectures to enhance system functionality and scalability. Lead the design and implementation of GenAI-driven applications, ensuring seamless integration with microservices and container-based environments. Create solutions that fully leverage the capabilities of modern microservice and container-based environments running in public, private, and hybrid clouds. Contribute to HCL thought leadership across the Cloud Native domain with an expert understanding of open-source technologies (e.g., Kubernetes/CNCF) and partner technologies. Collaborate on joint technical projects with partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware. Service Delivery Engineer innovative GenAI solutions from ideation to MVP, ensuring high performance and reliability within cloud-native frameworks. Optimize AI models for deployment in cloud environments, balancing efficiency and effectiveness to meet client requirements and industry standards. Assess existing complex solutions and recommend appropriate technical treatments to transform applications with cloud-native/12-factor characteristics. Refactor existing solutions to implement a microservices-based architecture. Innovation & Initiative Drive the adoption of cutting-edge GenAI technologies within cloud-native projects, spearheading initiatives that push the boundaries of AI integration in cloud services. Engage in technical innovation and support HCLs position as an industry leader. Author whitepapers, blogs, and speak at industry events. Maintain hands-on technical credibility, stay ahead of industry trends, and mentor others. Client Relationships Provide expert guidance to clients on incorporating GenAI and machine learning into their cloud-native systems, ensuring best practices and strategic alignment with business goals. Conduct workshops and briefings to educate clients on the benefits and applications of GenAI, establishing strong, trust-based relationships. Perform a trusted advisor role, contributing to technical projects (PoCs and MVPs) with a strong focus on technical excellence and on-time delivery. Mandatory Skills & Experience A passionate developer with 10+ years of experience in Java, Python, Node.js, and Spring programming, comfortable working as part of a paired/balanced team. Extensive experience in software development, with significant exposure to AI/ML technologies. Expertise in GenAI frameworks: Proficient in using GenAI frameworks and libraries such as LangChain, OpenAI API, Gemini, and Hugging Face Transformers. Prompt engineering: Experience in designing and optimizing prompts for various AI models to achieve desired outputs and improve model performance. Strong understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models. Proven experience developing complex solutions that leverage cloud-native technologiesfeaturing container-based, microservices-based approaches; based on applying 12-factor principles to application engineering. Exemplary verbal and written communication skills (English). Positive and solution-oriented mindset. Solid experience delivering Agile and Scrum projects in a Jira-based project management environment. Proven leadership skills and the ability to inspire and manage teams. Desired Skills & Experience Machine Learning Operations (MLOps): Experience in deploying, monitoring, and maintaining AI models in production environments using MLOps practices. Data engineering for AI: Skilled in data preprocessing, feature engineering, and creating pipelines to feed AI models with high-quality data. AI model fine-tuning: Proficiency in fine-tuning pre-trained models on specific datasets to improve performance for specialized tasks. AI ethics and bias mitigation: Knowledgeable about ethical considerations in AI and experienced in implementing strategies to mitigate bias in AI models. Knowledgeable about vector databases, LLMs, and SMLs, and integrating with such models. Proficient with Kubernetes and other cloud-native technologies, including experience with commercial Kubernetes distributions (e.g., Red Hat OpenShift, VMware Tanzu, Google Anthos, Azure AKS, Amazon EKS, Google GKE). Deep understanding of core practices including DevOps, SRE, Agile, Scrum, XP, Domain-Driven Design, and familiarity with the CNCF open-source community. Recognized with multiple cloud and technical certifications at a professional level, ideally including AI/ML specializations from providers like Google, Microsoft, AWS, Linux Foundation, IBM, or Red Hat. Verifiable Certification At least one recognized cloud professional / developer certification (AWS/Google/Microsoft)

We are looking for a skilled AI Engineer with 3 to 8 years of experience in software engineering or machine learning to design, implement, and productionize LLM-powered agents that solve real-world enterprise problems. This position is based in Kolkata. Roles and Responsibility Architect and build multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, Google ADK, Palantir Foundry, or custom orchestration layers. Fine-tune and prompt-engineer LLMs (OpenAI, Anthropic, open-source) for retrieval-augmented generation (RAG), reasoning, and tool use. Integrate agents with enterprise data sources (APIs, SQL/NoSQL DBs, vector stores like Pinecone, Elasticsearch) and downstream applications (Snowflake, ServiceNow, custom APIs). Own the MLOps lifecycle: containerize (Docker), automate CI/CD, monitor drift & hallucinations, set up guardrails, observability, and rollback strategies. Collaborate cross-functionally with product, UX, and customer teams to translate requirements into robust agent capabilities and user-facing features. Benchmark and iterate on latency, cost, and accuracy; design experiments, run A/B tests, and present findings to stakeholders. Job Requirements Strong Python skills (async I/O, typing, testing) plus familiarity with TypeScript/Node or Go is a bonus. Hands-on experience with at least one LLM/agent framework and platform (LangChain, LangGraph, Google ADK, LlamaIndex, Emma, etc.). Solid grasp of vector databases (Pinecone, Weaviate, FAISS) and embedding models. Experience building and securing REST/GraphQL APIs and microservices. Cloud skills on AWS, Azure, or GCP (serverless, IAM, networking, cost optimization). Proficient with Git, Docker, CI/CD (GitHub Actions, GitLab CI, or similar).