We are looking for a GenAI Engineer (LLM Engineer) to build and scale LLM-powered SaaS applications with a secure, scalable, and high-performance architecture. In this role, you will be responsible for integrating Large Language Models (LLMs) into production applications, developing intelligent AI workflows, and building solutions that leverage Retrieval-Augmented Generation (RAG), Prompt Engineering, and Vector Databases.

As an AI Integration Engineer, you will work on integrating LLM APIs, designing and optimizing RAG pipelines, managing Vector Databases, and collaborating with product and engineering teams to deliver scalable AI-powered features. You will also ensure application security, monitor performance, optimize latency and cost, and build reliable AI solutions that enhance the overall SaaS platform.

Key Responsibilities

• Integrate LLM APIs to build intelligent automation, parsing, and conversational AI solutions.
• Design and develop Retrieval-Augmented Generation (RAG) Pipelines.
• Create and optimize Prompt Engineering workflows for AI applications.
• Build, manage, and optimize Vector Databases for efficient knowledge retrieval.
• Ensure data security, compliance, and high-performance AI architecture.
• Monitor and optimize AI model quality, latency, and operational cost.
• Collaborate with Product and Engineering teams to deliver scalable AI-powered SaaS features.

Experience Required

• 3+ years of backend development experience using Python, Go, or Node.js.
• Experience building LLM Applications using OpenAI, Claude, or Hugging Face.
• Hands-on experience with LangChain or LlamaIndex.
• Strong understanding of Retrieval-Augmented Generation (RAG).
• Experience with Vector Databases such as Pinecone, Weaviate, or pgvector.
• Experience working with Cloud Platforms (AWS, GCP, or Azure).
• Knowledge of APIs, Microservices, and Prompt Engineering.

Preferred

• Experience in FinTech or SaaS domain.
• Exposure to LLMOps and Model Fine-tuning.

Skills Required

Must Have

• Model Integration.
• Retrieval-Augmented Generation (RAG) Pipelines.
• Vector Database implementation and management.
• Security and Compliance best practices.
• AI Performance Monitoring and Optimization.

Preferred

• CI/CD for AI Applications.
• Observability for AI systems.

Education & Qualification

• B.Tech / BCA or Technical Certification.

We are seeking a dynamic and technically driven Sales And Service Engineer to manage the complete customer lifecycle . You will identify new business opportunities , deliver technical presentations , and provide hands - on post-sales support . Your primary goal is to drive sales growth while ensuring maximum equipment uptime and customer satisfaction .

Key Responsibilities

Sales & Business Development

• Lead Generation :- Identify and develop new business opportunities across target industries
• Technical Presentations :- Deliver product demonstrations and explain complex technical concepts to prospective clients
• Proposal Management :- Prepare techno - commercial proposals , cost estimates , and tender documents
• Account Management :- Maintain strong relationships with existing clients to generate repeat business and upsell opportunities

Service & Technical Support

• Installation & Commissioning :- Perform on-site installation , configuration , and calibration of equipment
• Maintenance & Troubleshooting :- Conduct scheduled preventive maintenance and diagnose / resolve equipment faults or breakdowns
• Customer Training :- Educate client staff on proper equipment usage , safety procedures , and basic maintenance
• Feedback Loop :- Relay technical issues and customer feedback to the product development or engineering teams

Qualifications & Requirements

• Education :- Bachelor's Degree or Diploma in Engineering ( Mechanical )
• Experience :- 1-2 years of proven experience in technical sales and field service
• Technical Skills :- Deep understanding of relevant product lines , ability to read schematics / technical drawings , and proficiency with digital tools like CRM software
• Soft Skills :- Excellent communication , negotiation , and problem - solving abilities
• Requirements :- Willingness to travel frequently to client sites within the assigned territory
• Immediate joiners would be preferred

Test Materialisation & Execution Engineer

Location: Bangalore (On-site/Hybrid)

Experience: 5–10 Years

About the Role

We are looking for a skilled Test Materialisation & Execution Engineer to design, build, and enhance testing infrastructure that enables reliable and efficient software validation. The role involves working on test frameworks, execution environments, automation systems, and developer tooling to improve software quality at scale.

The ideal candidate is a strong problem solver with hands-on experience in testing ecosystems, build systems, and scalable execution environments. You should be comfortable working with complex codebases and building solutions that help engineering teams ship reliable software faster.

Key Responsibilities

• Design and develop test execution frameworks and automation solutions.
• Build and maintain systems for test discovery, execution, reporting, and validation.
• Work with test runners, build systems, and development ecosystems to improve testing workflows.
• Develop scalable and reliable execution environments using sandboxed or containerized technologies.
• Integrate testing solutions with CI/CD pipelines and engineering workflows.
• Analyze test performance, reliability, and effectiveness to improve developer productivity.
• Work with engineering teams to identify and resolve flaky tests and improve test coverage.
• Build tools that enable safe execution of generated or untrusted code at scale.

Must Have Skills

• Hands-on experience with test frameworks and runners in at least one major programming ecosystem (Java, .NET, or similar).
• Strong understanding of build systems and software execution environments.
• Experience with sandboxed or containerized execution approaches.
• Strong programming skills in Python and/or TypeScript.
• Experience designing and implementing automation solutions.
• Ability to understand complex software systems and deliver production-ready solutions.

Good to Have

• Experience with CI/CD pipeline internals.
• Knowledge of code coverage tools and test analytics.
• Experience with flaky test detection and mitigation strategies.
• Exposure to test-impact analysis techniques.
• Experience safely executing untrusted, generated, or dynamically created code.
• Experience building developer tools or engineering productivity platforms.

What We Look For

• Strong ownership mindset and ability to deliver end-to-end solutions.
• Fast learner who can quickly understand unfamiliar codebases and technologies.
• Passion for improving developer productivity and software quality.
• Ability to solve complex technical challenges with minimal supervision.

Work Location

Bangalore – On-site / Hybrid

Job Title: AI/ML Engineer

Location: Surat, Gujarat

Employment Type: Full-Time

Experience: 1–3 Years

About the Role

We are looking for a skilled AI/ML Engineer to design, develop, and deploy scalable, production-ready AI and Machine Learning solutions. The ideal candidate should have hands-on experience in Machine Learning, Deep Learning, Predictive Analytics, and modern AI technologies.

Key Responsibilities

• Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
• Build solutions for prediction, forecasting, classification, clustering, recommendation systems, and anomaly detection.
• Develop AI applications using LLMs, RAG, embeddings, LangChain, and vector databases.
• Perform data preprocessing, feature engineering, model evaluation, and performance optimization.
• Build and maintain AI/ML pipelines and integrate models with applications through APIs.
• Collaborate with product and engineering teams to develop AI-driven features.
• Monitor and improve deployed models and maintain relevant technical documentation.

Required Skills

• 1–3 years of experience in AI, Machine Learning, or Deep Learning.
• Strong knowledge of Python and AI/ML frameworks.
• Experience with data preprocessing, model training, evaluation, and deployment.
• Understanding of predictive analytics and modern AI application development.
• Familiarity with APIs and integrating ML models into applications.

Preferred Skills

• Experience with LLMs, RAG, LangChain, embeddings, and vector databases such as Pinecone, Milvus, Weaviate, Chroma, or FAISS.
• Knowledge of MLOps, Docker, Kubernetes, CI/CD, and cloud platforms such as AWS, Azure, or GCP.
• Experience or knowledge of Trading, FinTech, Wealth Management, Banking, or Financial Services is an advantage.
• Experience with time-series forecasting, financial datasets, market data, or predictive analytics is preferred.

Roles and Responsibilities

• Analyze business needs and translate them into actionable AI and automation opportunities across teams.
• Develop and document AI and automation strategies aligned with organizational goals, measurable outcomes, and operating constraints.
• Work with stakeholders to map end-to-end processes, identify bottlenecks, and define improvement targets.
• Support the evaluation of AI/ML use cases by assessing feasibility, expected value, data readiness, and integration impacts.
• Create clear requirements and success criteria for automation initiatives, including scope, dependencies, and delivery milestones.
• Partner with engineering, data, and operations teams to ensure solutions meet business workflows and compliance expectations.
• Contribute to solution design by documenting how models, automation logic, and business rules interact within current systems.
• Maintain and communicate project artifacts such as process maps, requirement documents, and stakeholder updates.
• Monitor progress and participate in iterative delivery by incorporating feedback and adjusting requirements as needed.
• Facilitate workshops and working sessions to gather inputs, resolve ambiguities, and drive consensus.

Required Qualifications

• Proven experience in business analysis within technology-driven environments, including automation or analytics initiatives.
• Strong understanding of AI and automation concepts, including how AI/ML systems function in real-world contexts.
• Demonstrated ability to map and document business processes (e.g., current state/future state) to support solution planning.
• Experience gathering requirements from multiple stakeholders and translating them into clear, testable deliverables.
• Ability to assess data and system readiness at a conceptual level (availability, quality considerations, integration points).
• Excellent communication skills with the ability to present complex ideas to both technical and non-technical audiences.
• Strong stakeholder management skills, including facilitation, negotiation, and expectation-setting.
• Comfort working in a remote environment with well-organized planning, documentation, and follow-through.
• Experience with project tracking, documentation, and structured problem-solving approaches.

Key Responsibilities

• Lead discovery and analysis activities for AI and automation initiatives, including process documentation and requirement definition.
• Define use case business requirements: objectives, user impact, operational workflow changes, and success metrics.
• Produce process maps and business workflow documentation to guide implementation and change management.
• Collaborate with technical teams to ensure requirements align with system capabilities, data constraints, and rollout plans.
• Support prioritization of initiatives by estimating value, effort, risk, and dependencies across stakeholders.
• Ensure that automation solutions are designed to fit into operational processes, including handoffs, exceptions, and governance.
• Document assumptions, open questions, and decision logs to maintain clarity throughout the delivery lifecycle.
• Validate outcomes by defining acceptance criteria, performance expectations, and measurement approaches.
• Continuously refine requirements based on stakeholder feedback, pilot results, and implementation learnings.