About the Role We seek a hands-on Delivery Lead with 10–12 years of experience to own end-to-end execution of AI/ML projects, mentor technical teams, and collaborate with sales to scale our consulting solutions. Key Responsibilities 1. Project Delivery Own client-facing AI/ML projects from scoping to deployment, ensuring quality, timelines, and business impact. Guide teams on architecture, model design, and deployment (cloud/on-prem). 2. Team Leadership Lead and mentor data scientists/engineers, fostering skill growth and collaboration. Drive best practices in AI/ML (LLMs, MLOps, Agile). 3. Sales Collaboration Partner with sales to scope proposals, estimate efforts, and present technical solutions to clients. Must-Have Skills 10–12 years in AI/ML, with 5+ years leading teams in consulting/client delivery. Expertise in: AI/ML frameworks (TensorFlow, PyTorch, LLMs). Cloud platforms (AWS/Azure/GCP) and CI/CD pipelines. Python/R, data engineering (Spark, Kafka). Strong communication and client/stakeholder management. Good to Have Familiarity with MLOps (Docker, Kubernetes). Agile/Scrum certification. Qualifications Bachelor’s/Master’s in CS, Data Science, or related field. Certifications (e.g., AWS ML, Azure AI Engineer) are a plus. About Us Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning solutions—spanning predictive analytics, optimization, NLP, and GenAI—that help leading enterprises forecast, automate, and gain a competitive edge. Join us to tackle high-impact, cross-industry projects where your ideas move rapidly from concept to production, shaping the future of data-driven decision-making. Show more Show less
Job Title: Senior Gen AI Engineer Location : Hyderabad Experience : 6–10 years Job Type : Full-time Reports To : Lead Data Scientist / Chief Scientist Job Overview We seek a Senior Gen AI Engineer with strong ML fundamentals and data engineering expertise to lead scalable AI/LLM solutions. This role focuses on integrating AI models into production, optimizing machine learning workflows, and creating scalable AI-driven systems. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices. Key Responsibilities Agentic AI & Workflow Design : Lead design and implementation of Agentic AI systems and multi-step AI workflows. Build AI orchestration systems using frameworks like LangGraph. Utilize Agents, Tools, and Chains for complex task automation. Implement Agent-to-Agent (A2A) communication and Model Connect Protocol (MCP) for inter-model interactions. Production MLOps & Deployment : Develop, train, and deploy ML models optimized for production. Implement CI/CD pipelines (GitHub), automated testing, and robust observability (monitoring, logging, tracing) for Gen AI solutions. Containerize models (Docker) and deploy on cloud (AWS/Azure/GCP) using Kubernetes. Implement robust AI/LLM security measures and adhere to Responsible AI principles. AI Model Integration : Integrate LLMs and models from HuggingFace. Apply deep learning concepts with PyTorch or TensorFlow. Data & Prompt Engineering : Build scalable data pipelines for unstructured/text data. Design and implement embedding/chunking strategies for scalable data processing. Optimize storage/retrieval for embeddings (e.g., Pinecone, Weaviate). Utilize Prompt Engineering techniques to fine-tune AI model performance. Solution Development : Develop GenAI-driven Text-to-SQL solutions. Collaboration & Best Practices : Mentor junior team members on ML engineering best practices. Apply Agile/Scrum methodologies for project delivery. Required Skills & Qualifications Education : BS/BTech or Master's degree in Computer Science, Data Science, AI, or ML (or equivalent experience). Experience : 6+ years in ML + data engineering , with 2+ years in LLM/GenAI projects. 3+ years developing machine learning models. Technical Skills : Programming : Python. Foundation Model APIs : AzureOpenAI, OpenAI, Gemini, Anthropic, or AWS Bedrock. Agentic AI & LLM Frameworks : LangChain, LangGraph, A2A, MCP, Chains, Tools, Agents. Ability to design multi-agent systems, autonomous reasoning pipelines, and tool-calling capabilities for AI agents. MLOps/LLMOps : Docker , Kubernetes (K8s) , CI/CD , Automated Testing, Monitoring, Observability, Model Registries, Data Versioning. Cloud Platforms : AWS/Azure/GCP. Vector Databases : Pinecone, Weaviate, or similar leading platforms. Prompt Engineering . Security & Ethics : AI/LLM solution security, Responsible AI principles. Version Control : GitHub. Databases : SQL/NoSQL. Good to have : Reinforcement learning, multi-modal AI systems, complex reasoning frameworks (e.g., ReAct, Chain-of-Thought, planning algorithms). Experience in large-scale multi-agent coordination. Knowledge of deep learning frameworks such as PyTorch or TensorFlow. Experience with Feature Stores. Skills Matrix Candidates must submit a detailed resume and fill out the following matrix: Skill, Details, Skills Last Used, Experience (months), Self-Rating (0–10) Python (must) ML Engineering (must) SQL/NoSQL (must) LLM Frameworks (LangChain, LangGraph) (must) MLOps/LLMOps (Docker/K8s, CI/CD, Automated Testing, Monitoring, Observability, Model Registries, Data Versioning) (must) Cloud (AWS/Azure/GCP) (must) Vector Databases (e.g., Pinecone, Weaviate, or similar leading platforms) (must) Prompt Engineering (must) AI Agent Design (Chains, Tools) (must) A2A / MCP (highly preferred) GitHub (must) Security (AI/LLM, Responsible AI) Foundation Model APIs Reinforcement Learning Multi-modal AI systems Complex Reasoning Frameworks PyTorch / TensorFlow Feature Stores Instructions for Candidates : Provide a detailed resume highlighting projects related to LLMs, data engineering, MLOps, GenAI applications, and CI/CD with GitHub. Fill out the matrix above with accurate dates, experience duration, and self-ratings Please send your resume to jobs@SoothsayerAnalytics.com About Us: Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning solutions—spanning predictive analytics, optimization, NLP, and GenAI—that help leading enterprises forecast, automate, and gain a competitive edge. Join us to tackle high-impact, cross-industry projects where your ideas move rapidly from concept to production, shaping the future of data-driven decision-making.
We seek a hands-on Delivery Lead with 1012 years of experience to own end-to-end execution of AI/ML projects, mentor technical teams, and collaborate with sales to scale our consulting solutions. Key Responsibilities 1. Project Delivery Own client-facing AI/ML projects from scoping to deployment, ensuring quality, timelines, and business impact. Guide teams on architecture, model design, and deployment (cloud/on-prem). 2. Team Leadership Lead and mentor data scientists/engineers, fostering skill growth and collaboration. Drive best practices in AI/ML (LLMs, MLOps, Agile). 3. Sales Collaboration Partner with sales to scope proposals, estimate efforts, and present technical solutions to clients. Must-Have Skills 10-12 years in AI/ML, with 5+ years leading teams in consulting/client delivery. Expertise in: o AI/ML frameworks (TensorFlow, PyTorch, LLMs). o Cloud platforms (AWS/Azure/GCP) and CI/CD pipelines. o Python/R, data engineering (Spark, Kafka). Strong communication and client/stakeholder management. Good to Have Familiarity with MLOps (Docker, Kubernetes). Agile/Scrum certification. Qualifications Bachelors/Masters in CS, Data Science, or related field. Certifications (e.g., AWS ML, Azure AI Engineer) are a plus.
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.
Company : Soothsayer Analytics Working Hours : Full-Time No. of Positions : 4 Locations : Hyderabad apply now apply now Position Overview About the Role: We are seeking a talented Generative AI/LLM Engineer with a strong background in building and deploying AI models, focusing on leveraging state-of-the-art technologies like Azure OpenAI GPT-4, GPT-4 Vision, or GPT-4 Omni. Experience with Retrieval-Augmented Generation (RAG) and working with Vector Databases is essential. While fine-tuning large language models (LLMs) is a plus, it is not mandatory. A general understanding of how deep learning models are trained or fine-tuned is required. The ideal candidate should be able to quickly learn and implement advanced techniques, even if they do not initially possess all the required experience. Key Responsibilities Design, develop, and deploy generative AI models using GPT-4 variants, including GPT-4 Vision and GPT-4 Turbo, tailored to address specific business needs. Implement and optimize Retrieval-Augmented Generation (RAG) techniques for enhanced data-driven solutions. Build and manage AI services using Python frameworks such as LangChain or LlamaIndex, and develop APIs with FastAPI or Quart for efficient integration. Focus on scalability, performance, and optimization of AI solutions across cloud environments, particularly with Azure and AWS. Work with Vector Databases (mandatory) and optionally Graph Databases for enhanced data management. Utilize Cosmos DB and SQL for robust data storage and management solutions. Apply MLOps or LLMOps practices to automate and streamline the AI model lifecycle, including CI/CD pipelines, monitoring, and maintenance. Implement and manage Azure Pipelines for continuous integration and deployment. Continuously research and adopt the latest advancements in AI, with a focus on quick learning and implementation of emerging technologies. Required Skills And Qualifications Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field. A minimum of 1+ years of experience specifically in Generative AI/LLM technologies, and 5+ years of experience in related fields. Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, FastAPI, or Quart. Expertise in Retrieval-Augmented Generation (RAG) and experience with Vector Databases (mandatory). Experience with Cosmos DB and SQL. Fine-tuning LLMs and experience with Graph Databases are good to have but not mandatory. Proven experience in MLOps, LLMOps, or DevOps with a strong understanding of CI/CD processes, automation, and pipeline management. Familiarity with containers, Docker, or Kubernetes is a plus. Familiarity with cloud platforms, particularly Azure or AWS, and experience with cloud-native AI services. Strong problem-solving abilities and a proactive approach to learning new AI trends and best practices quickly.
Working Hours : Full Time Locations : India (Remote) Experience : 5 years apply now apply now About The Role Work with team members to help respond/ creation of Statement of Work (SOWs), Request for Proposals (RFPs), Request for Quotations (RFQs), Requests for Information (RFIs), and manage the proposal development process to ensure on-time delivery of compelling, compliant, and well-written proposals. Exhibit strong writing skills, excellent attention to detail, thrive in a fast-past environment, be able to track multiple ongoing projects and responsibilities simultaneously. Self-starter, multitask-er who works well under pressure, and follows through on tasks. Maintain, augment, and improve the library of reusable components, templates, and processes to reduce response time and improve quality on future proposals. Assist with management of proposal deadlines by establishing priorities and target dates for information gathering, writing, review, and approval; entering and monitoring tracking data; coordinating requirements with contributors; contributing proposal status information to review meetings; transmitting proposals. Assist with developing & preparing proposals by assembling information including project nature, objectives/value proposition/deliverables, implementation, methods, timetable, and evaluation; writing, revising, and editing drafts including executive summaries, conclusions, and organization credentials. Obtain approvals by reviewing proposals with key team members and project managers as required. Improve proposal-writing results by evaluating and re-designing processes, approach, coordination, and boilerplate; implementing changes. Direct proposal kickoff and debrief meetings, for hand-off to project managers. Follow up on unsuccessful proposals to develop recommendations for improving the success rate. Coordinate delivery of the document to ensure submission deadlines are met. Technical Writer Develop materials / contents for websites, case studies, technical documentation, speaking engagements, workshops, Social media posts, and/or webinars. Study solutions and talk with the solution implementation team to create content for the solution. Incorporate animation, graphs, illustrations, or photographs to increase users’ understanding of the material. Select appropriate media, such as manuals or videos, for message or audience. Create digital operating instructions, how-to manuals, assembly instructions, and frequently asked questions. Key Skills Word, Powerpoint, Exel, Data Science Education & Experience Bachelor’s degree in a related field. 5+ years of experience in technical writing. 5+ years of experience in proposal writing. Experience writing proposals and/or knowledge of Data Science (AI, ML) is a big plus. Excellent communication skills, both verbal and written. Extensive experience reading and analyzing RFP requirements; ability to effectively identify and develop proposal. Proven experience working independently to produce draft materials under tight deadlines. Capable of setting and meeting deadlines. Ability to work well in both independent and highly collaborative settings. Ability to manage multiple writing projects concurrently. Must be adept at independent research to support written deliverables.
We seek a Senior Gen AI Engineer with strong ML fundamentals and data engineering expertise to lead scalable AI/LLM solutions. This role focuses on integrating AI models into production, optimizing machine learning workflows, and creating scalable AI-driven systems. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices. Key Responsibilities Agentic AI & Workflow Design: Lead design and implementation of Agentic AI systems and multi-step AI workflows. Build AI orchestration systems using frameworks like LangGraph. Utilize Agents, Tools, and Chains for complex task automation. Implement Agent-to-Agent (A2A) communication and Model Connect Protocol (MCP) for inter-model interactions. Production MLOps & Deployment: Develop, train, and deploy ML models optimized for production. Implement CI/CD pipelines (GitHub), automated testing, and robust observability (monitoring, logging, tracing) for Gen AI solutions. Containerize models (Docker) and deploy on cloud (AWS / Azure/ GCP) using Kubernetes. Implement robust AI/LLM security measures and adhere to Responsible AI principles. AI Model Integration: Integrate LLMs and models from HuggingFace. Apply deep learning concepts with PyTorch or TensorFlow. Data & Prompt Engineering: Build scalable data pipelines for unstructured/text data. Design and implement embedding/chunking strategies for scalable data processing. Optimize storage/retrieval for embeddings (e.g., Pinecone, Weaviate). Utilize Prompt Engineering techniques to fine-tune AI model performance. Solution Development: Develop GenAI-driven Text-to-SQL solutions. Programming: Python. Foundation Model APIs: AzureOpenAI, OpenAI, Gemini, Anthropic, or AWS Bedrock. Agentic AI & LLM Frameworks: LangChain, LangGraph, A2A, MCP, Chains, Tools, Agents. Ability to design multi-agent systems, autonomous reasoning pipelines, and tool-calling capabilities for AI agents. MLOps/LLMOps: Docker , Kubernetes (K8s) , CI/CD , Automated Testing, Monitoring, Observability, Model Registries, Data Versioning. Cloud Platforms: AWS/Azure/GCP. Vector Databases: Pinecone, Weaviate, or similar leading platforms. Prompt Engineering. Security & Ethics: AI/LLM solution security, Responsible AI principles. Version Control: GitHub. Databases: SQL/NoSQL.
We seek a Senior Full Stack AI Engineer passionate about rapidly developing and demonstrating Agentic AI and workflow solutions. This role focuses on quickly translating innovative ideas into tangible Proofs of Concept (POCs), leveraging full-stack capabilities to build attractive user interfaces, integrate sophisticated AI agents, and deploy working solutions for impactful demonstrations. Key Responsibilities Rapid Agentic AI POC Development: Lead agile development of end-to-end Agentic AI and complex workflow POCs from concept to demonstration, ensuring rapid iteration and delivery. Design and implement multi-turn conversational Agents, Tools, and Chains. Full Stack Application Development: Develop attractive and intuitive front-end user interfaces (e.g., React, Next.js) and robust back-end APIs (e.g., FastAPI, Flask). Implement comprehensive testing strategies across the full stack. AI Orchestration & Integration: Utilize frameworks like Langgraph (or equivalents such as CrewAI, AutoGen) to orchestrate complex AI agent systems and multi-step workflows. Implement Agent-to-Agent (A2A) communication and adhere to Model Connect Protocol (MCP) for inter-model interactions. Apply advanced prompt engineering techniques. Deployment & Demonstration: Containerize applications (Docker) and deploy on Kubernetes for scalable POCs. Establish and utilize CI/CD pipelines for continuous integration and delivery. Prepare and deliver compelling technical demonstrations. Experience: 6+ years in ML + data engineering, with 2+ years in LLM/GenAI projects. Proven rapid prototyping track record. Technical Skills: Programming: Python, JavaScript/TypeScript (React, Next.js). AI/ML Fundamentals: LLMs, prompt engineering, Agentic AI architectures. Agentic AI & LLM Frameworks: Langgraph, CrewAI, AutoGen, A2A, MCP, Chains, Tools, Agents. Front-end: React, Next.js. Back-end: FastAPI, Flask. Databases: Vector databases (Pinecone, Weaviate), SQL/NoSQL. DevOps: Docker, Kubernetes (K8s), CI/CD, Testing. Cloud Platforms: AWS/Azure/GCP. Good to have: Experience with cloud-native AI/ML services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI). Familiarity with UI/UX design principles for creating engaging interfaces. Experience with asynchronous programming patterns in Python. Knowledge of caching mechanisms (e.g., Redis) for optimizing application performance. Understanding of general Machine Learning concepts and common algorithms beyond LLMs.
You will be working as a Mid-Level Full Stack Developer at Soothsayer Analytics in Hyderabad. Your main responsibility will be to contribute to the development of Generative AI applications using Azure and OpenAI technologies. This role requires you to work on both front-end and back-end components of AI applications, leveraging React and Python, and integrating Azure OpenAI models, Bing Search, and Azure ML Studio into custom-built applications. Your key responsibilities include developing and maintaining front-end and back-end components of AI applications, integrating Azure OpenAI models and Azure services, working with databases such as Cosmos DB, SQL Server, and Weaviate, developing secure and scalable applications using VNet and Private Endpoint configurations, implementing APIs for interaction with large language models and document processing systems, collaborating with AI engineers to design and deploy Generative AI solutions, and implementing CI/CD automation using Azure DevOps and GitHub. To be successful in this role, you should have 3+ years of professional experience in full-stack development, strong proficiency in front-end development using React and back-end development with Python, experience with Azure services, familiarity with integrating AI models, experience with cloud security practices, knowledge of containerization and orchestration, familiarity with CI/CD pipelines and version control, and ideally, experience with Weaviate, LangChain, Hugging Face, or Neo4j databases. If you are passionate about developing innovative AI applications and have the required skills and experience, we encourage you to apply for this exciting opportunity at Soothsayer Analytics.,
You will be working full-time at Soothsayer Analytics in Hyderabad as a Generative AI/LLM Engineer. Your primary responsibility will be to design, develop, and deploy generative AI models using cutting-edge technologies like Azure OpenAI GPT-4, GPT-4 Vision, or GPT-4 Omni. You should have a strong background in building and deploying AI models, with a focus on leveraging technologies such as Retrieval-Augmented Generation (RAG) and working with Vector Databases. While experience in fine-tuning large language models (LLMs) is beneficial, it is not mandatory. You are expected to have a general understanding of training or fine-tuning deep learning models and be able to quickly learn and implement advanced techniques. Your key responsibilities will include designing, developing, and deploying generative AI models using GPT-4 variants, implementing and optimizing RAG techniques, building and managing AI services using Python frameworks like LangChain or LlamaIndex, and developing APIs with FastAPI or Quart for efficient integration. You will focus on scalability, performance, and optimization of AI solutions across cloud environments, particularly with Azure and AWS. Working with Vector Databases is mandatory, and experience with Graph Databases is optional. You will also utilize Cosmos DB and SQL for data storage and management solutions, apply MLOps or LLMOps practices, and manage Azure Pipelines for continuous integration and deployment. Additionally, you will continuously research and adopt the latest advancements in AI technologies. To qualify for this role, you should have a Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field, with at least 1+ years of experience in Generative AI/LLM technologies and 5+ years of experience in related fields. Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, FastAPI, or Quart is required. Expertise in RAG and experience with Vector Databases are mandatory. Knowledge of Cosmos DB and SQL is also essential. Experience with fine-tuning LLMs and Graph Databases is beneficial but not mandatory. You should have proven experience in MLOps, LLMOps, or DevOps, a strong understanding of CI/CD processes, automation, and pipeline management, and familiarity with containers, Docker, or Kubernetes. Experience with cloud platforms, particularly Azure or AWS, and cloud-native AI services is desirable. Strong problem-solving abilities and a proactive approach to learning new AI trends and best practices quickly are necessary for this role.,
We seek a Senior Gen AI Engineer with strong ML fundamentals and data engineering expertise to lead scalable AI/LLM solutions. This role focuses on integrating AI models into production, optimizing machine learning workflows, and creating scalable AI-driven systems. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices. Key Responsibilities Agentic AI & Workflow Design: Lead design and implementation of Agentic AI systems and multi-step AI workflows. Build AI orchestration systems using frameworks like LangGraph. Utilize Agents, Tools, and Chains for complex task automation. Implement Agent-to-Agent (A2A) communication and Model Connect Protocol (MCP) for inter-model interactions. Production MLOps & Deployment: Develop, train, and deploy ML models optimized for production. Implement CI/CD pipelines (GitHub), automated testing, and robust observability (monitoring, logging, tracing) for Gen AI solutions. Containerize models (Docker) and deploy on cloud (AWS / Azure/ GCP) using Kubernetes. Implement robust AI/LLM security measures and adhere to Responsible AI principles. AI Model Integration: Integrate LLMs and models from HuggingFace. Apply deep learning concepts with PyTorch or TensorFlow. Data & Prompt Engineering: Build scalable data pipelines for unstructured/text data. Design and implement embedding/chunking strategies for scalable data processing. Optimize storage/retrieval for embeddings (e.g., Pinecone, Weaviate). Utilize Prompt Engineering techniques to fine-tune AI model performance. Solution Development: Develop GenAI-driven Text-to-SQL solutions. Programming: Python. Foundation Model APIs: AzureOpenAI, OpenAI, Gemini, Anthropic, or AWS Bedrock. Agentic AI & LLM Frameworks: LangChain, LangGraph, A2A, MCP, Chains, Tools, Agents. Ability to design multi-agent systems, autonomous reasoning pipelines, and tool-calling capabilities for AI agents. MLOps/LLMOps: Docker , Kubernetes (K8s) , CI/CD , Automated Testing, Monitoring, Observability, Model Registries, Data Versioning. Cloud Platforms: AWS/Azure/GCP. Vector Databases: Pinecone, Weaviate, or similar leading platforms. Prompt Engineering. Security & Ethics: AI/LLM solution security, Responsible AI principles. Version Control: GitHub. Databases: SQL/NoSQL.
We seek a hands-on Delivery Lead with 1012 years of experience to own end-to-end execution of AI/ML projects, mentor technical teams, and collaborate with sales to scale our consulting solutions. Key Responsibilities 1. Project Delivery Own client-facing AI/ML projects from scoping to deployment, ensuring quality, timelines, and business impact. Guide teams on architecture, model design, and deployment (cloud/on-prem). 2. Team Leadership Lead and mentor data scientists/engineers, fostering skill growth and collaboration. Drive best practices in AI/ML (LLMs, MLOps, Agile). 3. Sales Collaboration Partner with sales to scope proposals, estimate efforts, and present technical solutions to clients. Must-Have Skills 10-12 years in AI/ML, with 5+ years leading teams in consulting/client delivery. Expertise in: o AI/ML frameworks (TensorFlow, PyTorch, LLMs). o Cloud platforms (AWS/Azure/GCP) and CI/CD pipelines. o Python/R, data engineering (Spark, Kafka). Strong communication and client/stakeholder management. Good to Have Familiarity with MLOps (Docker, Kubernetes). Agile/Scrum certification. Qualifications Bachelors/Masters in CS, Data Science, or related field. Certifications (e.g., AWS ML, Azure AI Engineer) are a plus.
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.
You will be joining Soothsayer Analytics, a global AI & Data Science consultancy based in Detroit, with a thriving delivery center in Hyderabad. The company specializes in designing and implementing custom Machine Learning solutions encompassing predictive analytics, optimization, NLP, and GenAI to assist leading enterprises in forecasting, automation, and gaining a competitive advantage. As a Senior AI Scientist, your role will involve leading the development of scalable AI/LLM solutions. You will be responsible for designing, fine-tuning, and deploying models such as LLMs and RAG architectures, while ensuring the robustness of data pipelines and MLOps practices. Your key responsibilities will include: - Fine-tuning and optimizing LLMs and traditional ML models for production - Implementing retrieval-augmented generation (RAG) and orchestration tools - Building scalable data pipelines for unstructured/text data - Optimizing storage/retrieval for embeddings - Containerizing models and deploying them on cloud platforms using Kubernetes - Designing CI/CD pipelines for LLM workflows - Collaborating with DevOps to optimize latency/cost trade-offs and mentoring junior team members To qualify for this role, you should have: - Education: MS/PhD in CS/AI/Data Science or equivalent experience - Experience: 6+ years in ML and data engineering, with at least 2 years in LLM/GenAI projects If you are enthusiastic about working on high-impact, cross-industry projects that involve cutting-edge AI technologies and want to contribute to the future of data-driven decision-making, we encourage you to share your resume with srikanth.bojja@soothsayeranalytics.com.,
We are looking for a hands-on Delivery Lead with 10-12 years of experience to take charge of end-to-end execution of AI/ML projects, provide guidance to technical teams, and collaborate with sales to expand our consulting solutions. In this role, you will be responsible for project delivery by managing client-facing AI/ML projects from scoping to deployment, ensuring high quality, meeting timelines, and delivering business impact. You will also be guiding teams on architecture, model design, and deployment on cloud or on-premises environments. As a Team Leader, you will lead and mentor data scientists and engineers, encouraging skill development and fostering collaboration within the team. You will drive the adoption of best practices in AI/ML such as LLMs, MLOps, and Agile methodologies. Collaboration with the sales team is a key aspect of this role. You will partner with sales to define project scopes, estimate efforts, and present technical solutions to clients effectively. The ideal candidate should have 10-12 years of experience in AI/ML, with at least 5 years of experience leading teams in consulting or client delivery. Proficiency in AI/ML frameworks like TensorFlow, PyTorch, LLMs, cloud platforms such as AWS, Azure, GCP, CI/CD pipelines, Python/R, data engineering tools like Spark, Kafka, as well as strong communication and client/stakeholder management skills are essential. Additionally, familiarity with MLOps tools like Docker, Kubernetes, and Agile/Scrum certification would be advantageous. A Bachelor's or Master's degree in Computer Science, Data Science, or a related field, along with certifications such as AWS ML or Azure AI Engineer, would be a plus. Join us at Soothsayer Analytics, a global AI & Data Science consultancy based in Detroit with a thriving delivery center in Hyderabad. We specialize in designing and implementing custom end-to-end Machine Learning solutions covering predictive analytics, optimization, NLP, and GenAI, empowering leading enterprises to predict, automate, and gain a competitive advantage. If you are looking to work on impactful, cross-industry projects where your ideas swiftly transition from concept to production, influencing the future of data-driven decision-making, then we welcome you to join our team.,
Job Title: Junior AI Engineer Location: Hyderabad Experience: Min 2 years Job Type: Full-time Reports To: Senior AI Engineer / Lead Data Scientist About Us Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning & GenAI solutions—spanning predictive analytics, optimization, NLP, computer vision, and enterprise-scale AI platforms—that help leading enterprises forecast, automate, and gain a competitive edge. This is a unique opportunity to begin your AI career by working on cutting-edge GenAI, ML, and data-driven projects across industries. Job Overview We seek a Junior AI Engineer with min 2years of hands-on experience (or strong internships/projects) in machine learning, NLP, or GenAI. You will support the development and deployment of AI models, contribute to data preparation pipelines, and learn best practices in MLOps and applied AI engineering. Key Responsibilities: Model Development & Support Assist in building ML models (classification, regression, clustering, forecasting). Contribute to fine-tuning and prompt engineering for LLMs (GPT, LLaMA, etc.). Experiment with vector databases and RAG pipelines for GenAI applications. Data Preparation & Engineering Work with structured/unstructured datasets for ML training. Perform data cleaning, feature engineering, and basic pipeline development. MLOps & Deployment (Learning Role) Support containerization and deployment of models using Docker/Kubernetes. Learn to implement CI/CD pipelines for ML workflows. Collaboration & Learning Work under guidance of senior engineers and data scientists. Document experiments and present results to technical and business stakeholders. Required Skills & Qualifications Education: Bachelor’s in Computer Science, AI, Data Science, or related field. Experience: 1–2 years (including internships, academic projects, or full-time roles). Technical Skills: Programming: Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow). ML Basics: Regression, classification, clustering, time-series. GenAI/LLMs: Prompt engineering, LangChain basics, RAG concepts. Databases: SQL (basic), exposure to NoSQL/vector DBs (Pinecone, FAISS). MLOps Exposure: Docker, Git, basic CI/CD knowledge. Bonus: Knowledge of vision models, transformers, or cloud AI services (AWS Sagemaker, Azure ML, GCP Vertex AI). Please submit skills matrix in tabular format having the following columns: Skill, Details, Skills Last Used, Experience (months), Self-Rating (0–10) Python (pandas, sklearn, PyTorch/TensorFlow) ML Basics (Regression, Clustering, etc.) GenAI/LLMs (Prompting, LangChain, etc.) SQL / NoSQL Vector Databases (FAISS, Pinecone, pgvector) MLOps Tools (Docker, Git, CI/CD) Cloud AI Platforms (AWS/Azure/GCP) Instructions for Candidates: Provide a detailed resume highlighting academic/ industry projects in ML and AI . Fill out the above skills matrix with accurate details and self-ratings.
Position Overview: We are seeking a talented Generative AI/LLM Engineer with a strong background in building and deploying AI models, focusing on leveraging state-of-the-art technologies like Azure OpenAI GPT-4, GPT-4 Vision, or GPT-4 Omni. Experience with Retrieval-Augmented Generation (RAG) and working with Vector Databases is essential. While fine-tuning large language models (LLMs) is a plus, it is not mandatory. A general understanding of how deep learning models are trained or fine-tuned is required. The ideal candidate should be able to quickly learn and implement advanced techniques, even if they do not initially possess all the required experience. Key Responsibilities: Design, develop, and deploy generative AI models using GPT-4 variants, including GPT-4 Vision and GPT-4 Turbo, tailored to address specific business needs. Implement and optimize Retrieval-Augmented Generation (RAG) techniques for enhanced data-driven solutions. Build and manage AI services using Python frameworks such as LangChain or LlamaIndex, and develop APIs with FastAPI or Quart for efficient integration. Focus on scalability, performance, and optimization of AI solutions across cloud environments, particularly with Azure and AWS. Work with Vector Databases (mandatory) and optionally Graph Databases for enhanced data management. Utilize Cosmos DB and SQL for robust data storage and management solutions. Apply MLOps or LLMOps practices to automate and streamline the AI model lifecycle, including CI/CD pipelines, monitoring, and maintenance. Implement and manage Azure Pipelines for continuous integration and deployment. Continuously research and adopt the latest advancements in AI, with a focus on quick learning and implementation of emerging technologies. Required Skills and Qualifications: Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field. A minimum of 1+ years of experience specifically in Generative AI/LLM technologies, and 5+ years of experience in related fields. Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, FastAPI, or Quart. Expertise in Retrieval-Augmented Generation (RAG) and experience with Vector Databases (mandatory). Experience with Cosmos DB and SQL. Fine-tuning LLMs and experience with Graph Databases are good to have but not mandatory. Proven experience in MLOps, LLMOps, or DevOps with a strong understanding of CI/CD processes, automation, and pipeline management. Familiarity with containers, Docker, or Kubernetes is a plus. Familiarity with cloud platforms, particularly Azure or AWS, and experience with cloud-native AI services. Strong problem-solving abilities and a proactive approach to learning new AI trends and best practices quickly.
We seek a Junior AI Engineer with min 2years of hands-on experience (or strong internships/projects) in machine learning, NLP, or GenAI. You will support the development and deployment of AI models, contribute to data preparation pipelines, and learn best practices in MLOps and applied AI engineering. Key Responsibilities 1. Model Development & Support Assist in building ML models (classification, regression, clustering, forecasting). Contribute to fine-tuning and prompt engineering for LLMs (GPT, LLaMA, etc.). Experiment with vector databases and RAG pipelines for GenAI applications. 2. Data Preparation & Engineering Work with structured/unstructured datasets for ML training. Perform data cleaning, feature engineering, and basic pipeline development. 3. MLOps & Deployment (Learning Role) Support containerization and deployment of models using Docker/Kubernetes. Learn to implement CI/CD pipelines for ML workflows. 4. Collaboration & Learning Work under guidance of senior engineers and data scientists. Document experiments and present results to technical and business stakeholders. Technical Skills: o Programming: Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow). o ML Basics: Regression, classification, clustering, time-series. o GenAI/LLMs: Prompt engineering, LangChain basics, RAG concepts. o Databases: SQL (basic), exposure to NoSQL/vector DBs (Pinecone, FAISS). o MLOps Exposure: Docker, Git, basic CI/CD knowledge. Bonus: Knowledge of vision models, transformers, or cloud AI services (AWS Sagemaker, Azure ML, GCP Vertex AI). Education- Bachelors/Masters in Computer Science, Data Engineering, or related field
We seek a Data Engineer (Mid-level) with 46 years of hands-on experience in designing, building, and optimizing data pipelines. You will work closely with AI/ML teams to ensure data availability, quality, and performance for analytics and GenAI use cases.Key Responsibilities 1. Data Pipeline Development Build and maintain scalable ETL/ELT pipelines for structured and unstructured data. Ingest data from diverse sources (APIs, streaming, batch systems). 2. Data Modeling & Warehousing Design efficient data models to support analytics and AI workloads. Develop and optimize data warehouses/lakes using Redshift, BigQuery, Snowflake, or Delta Lake. 3. Big Data & Streaming Work with distributed systems like Apache Spark, Kafka, or Flink for real-time/large-scale data processing. Manage feature stores for ML pipelines. 4. Collaboration & Best Practices Work closely with Data Scientists and ML Engineers to ensure high-quality training data. Implement data quality checks, observability, and governance frameworks. o Programming: Python/Scala/Java (Python preferred). o Big Data & Processing: Apache Spark, Kafka, Hadoop. o Databases: SQL/NoSQL (Postgres, MongoDB, Cassandra). o Data Warehousing: Snowflake, Redshift, BigQuery, or similar. o Orchestration: Airflow, Luigi, or similar. o Cloud Platforms: AWS, Azure, or GCP (data services). o Version Control & CI/CD: Git, Jenkins, GitHub Actions. o MLOps/GenAI pipelines (feature engineering, embeddings, vector DBs).
We seek a Senior Cloud Engineer to design, build, and optimize scalable, secure, and cost-efficient cloud environments. Youll collaborate with AI/ML teams to deliver production-grade systems, automate deployments, and ensure resilience of data pipelines, APIs, and AI services across AWS, Azure, and GCP. This is a hands-on role where cloud architecture meets engineering excellence. Key Responsibilities 1. Cloud Architecture & Infrastructure Design and implement cloud-native solutions on AWS, Azure, or GCP. Build secure, highly available, and cost-optimized cloud infrastructure. Implement networking, IAM, security, and compliance best practices. 2. Automation & DevOps Develop Infrastructure as Code (IaC) using Terraform/CloudFormation. Implement CI/CD pipelines to automate deployments for AI/ML and data platforms. Enable monitoring, logging, and alerting using cloud-native or third-party tools. 3. Containerization & Orchestration Manage Kubernetes clusters and containerized workloads (Docker, EKS/AKS/GKE). Optimize workloads for scalability, performance, and cost efficiency. 4. Collaboration & Support Partner with Data & AI teams to ensure cloud infra supports ML/LLM workloads (e.g., GPU provisioning, vector DB hosting). Troubleshoot complex production issues and optimize cloud operations. Mentor junior engineers on cloud best practices. o Cloud Platforms: AWS, Azure, or GCP (multi-cloud experience preferred). o Infrastructure as Code: Terraform, CloudFormation, Pulumi. o Containers & Orchestration: Docker, Kubernetes, Helm. o CI/CD Tools: Jenkins, GitHub Actions, GitLab CI, or Azure DevOps. o Networking & Security: VPCs, IAM, firewalls, VPN, secrets management. o Observability: Prometheus, Grafana, ELK, or cloud-native monitoring tools. o AI/ML Enablement (preferred): GPU provisioning, supporting MLOps pipelines.