We’re Hiring: Lead AI/ML Engineer – GenAI + Azure (Remote) Python | Azure AI | LLMs, Agents, RAG | Remote Why join us Direct exposure to billion-dollar conglomerates Weekly AI deep-dives & learning sessions Fully remote, flexible execution High ownership, zero politics, career-defining impact At Sereno Volante , we build production-grade GenAI systems for India’s largest conglomerates—think JSW-scale. Our lean, high-agency team solves high-stakes enterprise problems at speed and scale. We’re looking for a Lead AI/ML Engineer to drive Azure-native GenAI deployments —from RAG stacks to agentic workflows—built for scale and resilience. What you’ll do Architect and ship GenAI systems using LLMs, agents, embeddings, RAG pipelines, vector search Leverage Azure services: App Service, AI Search, OpenAI, Azure Functions, Storage, Key Vault Lead a small, focused team and engage with enterprise product leads Own engineering from whiteboard to production—ship tools that drive real business outcomes What you bring 2–3 years of experience in Python + Azure-based AI/ML environments Hands-on with prompt engineering, LangChain, vector DBs, orchestration frameworks Strong understanding of LLM system design —retrieval pipelines, agent architecture, cost-performance tradeoffs Bonus: Familiar with enterprise platforms like Teams, Salesforce, or SAP Clear communicator, self-directed executor, and team-first mindset Show more Show less

We’re Hiring: Lead AI/ML Engineer – GenAI + Azure (Remote) Python | Azure AI | LLMs, Agents, RAG | Remote Why join us Direct exposure to billion-dollar conglomerates Weekly AI deep-dives & learning sessions Fully remote, flexible execution High ownership, zero politics, career-defining impact At Sereno Volante, we build production-grade GenAI systems for India’s largest conglomerates—think JSW-scale. Our lean, high-agency team solves high-stakes enterprise problems at speed and scale. We’re looking for a Lead AI/ML Engineer to drive Azure-native GenAI deployments—from RAG stacks to agentic workflows—built for scale and resilience. What you’ll do Architect and ship GenAI systems using LLMs, agents, embeddings, RAG pipelines, vector search Leverage Azure services: App Service, AI Search, OpenAI, Azure Functions, Storage, Key Vault Lead a small, focused team and engage with enterprise product leads Own engineering from whiteboard to production—ship tools that drive real business outcomes What you bring 2–3 years of experience in Python + Azure-based AI/ML environments Hands-on with prompt engineering, LangChain, vector DBs, orchestration frameworks Strong understanding of LLM system design—retrieval pipelines, agent architecture, cost-performance tradeoffs Bonus: Familiar with enterprise platforms like Teams, Salesforce, or SAP Clear communicator, self-directed executor, and team-first mindset Show more Show less

Who you are You're someone who’s already shipped GenAI stuff—even if it was small: a chatbot, a RAG tool, or an agent prototype. You live in Python, LangChain, LlamaIndex, Hugging Face, and vector DBs like FAISS or Milvus. You know your way around prompts—noisy chains, rerankers, retrievals. You've deployed models or services on Azure/AWS/GCP, wrapped them into FastAPI endpoints, and maybe even wired a bit of terraform/ARM. You’re not building from spreadsheets; you're iterating with real data, debugging hallucinations, and swapping out embeddings in production. You can read blog posts and paper intros, follow new methods like QLoRA, and build on them. You're fine with ambiguity and startup chaos—no strict specs, no roadmap, just a mission. You work in async Slack, ask quick questions, push code that works, and help teammates stay afloat. You're not satisfied with just getting things done—you want GenAI to feel reliable, usable, and maybe even fun. What you’ll actually do You’ll build real GenAI features: agentic chatbots for document lookup, conversation assistants, or knowledge workflows. You’ll design and implement RAG systems: data ingestion, embeddings, vector indexing, retrievals, and prompt pipelines. You’ll write inference APIs in FastAPI that work with vector stores and cloud LLM endpoints. You’ll containerize services with Docker, push to Azure/AWS/GCP, wire basic CI/CD, monitor latency and faulty responses, and iterate fast. You’ll experiment with LoRA/QLoRA fine-tuning on small LLMs, test prompt variants, and measure output quality. You’ll collaborate with DevOps to ensure deployment reliability, QA to make tests more robust, and frontend folks to shape UX. You’ll share your work in quick “demo & dish” sessions: what's working, what's broken, what you're trying next. You’ll tweak embeddings, watch logs, and improve pipelines one experiment at a time. You’ll help write internal docs or “how-tos” so others can reuse your work. Skills and knowledge You have solid experience in Python backend development (FastAPI/Django) Experienced with LLM frameworks: LangChain, LlamaIndex, CrewAI, or similar Comfortable with vector databases: FAISS, Pinecone, Milvus Able to fine-tune models using PEFT/LoRA/QLoRA Knowledge of embeddings, retrieval systems, RAG pipelines, and prompt engineering Familiar with cloud deployment and infra-as-code (Azure, AWS, GCP with Docker/K8s, Terraform/ARM) Good understanding of monitoring and observability—tracking response latency, hallucinations, and costs Able to read current research, try prototypes, and apply them pragmatically Works well in minimal-structure startups; self-driven, team-minded, proactive communicator

*Who you are* You’re the person whose fingertips know the difference between spinning up a GPU cluster and spinning down a stale inference node. You love the “infrastructure behind the magic” of LLMs. You've built CI/CD pipelines that automatically version models, log inference metrics, and alert on drift. You’ve containerized GenAI services in Docker, deployed them on Kubernetes clusters (AKS or EKS), and implemented terraform or ARM to manage infra-as-code. You monitor cloud costs like a hawk, optimize GPU workloads, and sometimes sacrifice cost for performance—but never vice versa. You’re fluent in Python and Bash, can script tests for REST endpoints, and build automated feedback loops for model retraining. You’re comfortable working in Azure — OpenAI, Azure ML, Azure DevOps Pipelines—but are cloud-agnostic enough to cover AWS or GCP if needed. You read MLOps/LLMOps blog posts or arXiv summaries on the weekend and implement improvements on Monday. You think of yourself as a self-driven engineer: no playbooks, no spoon-feeding—just solid automation, reliability, and a hunger to scale GenAI from prototype to production. --- *What you will actually do* You’ll architect and build deployment platforms for internal LLM services: start from containerizing models and building CI/CD pipelines for inference microservices. You’ll write IaC (Terraform or ARM) to spin up clusters, endpoints, GPUs, storage, and logging infrastructure. You’ll integrate Azure OpenAI and Azure ML endpoints, pushing models via pipelines, versioning them, and enabling automatic retraining triggers. You’ll build monitoring and observability around latency, cost, error rates, drift, and prompt health metrics. You’ll optimize deployments—autoscaling, use of spot/gpu nodes, invalidation policies—to balance cost and performance. You’ll set up automated QA pipelines that validate model outputs (e.g. semantic similarity, hallucination detection) before merging. You’ll collaborate with ML, backend, and frontend teams to package components into release-ready backend services. You’ll manage alerts, rollbacks on failure, and ensure 99% uptime. You'll create reusable tooling (CI templates, deployment scripts, infra modules) to make future projects plug-and-play. --- *Skills and knowledge* Strong scripting skills in Python and Bash for automation and pipelines Fluent in Docker, Kubernetes (especially AKS), containerizing LLM workloads Infrastructure-as-code expertise: Terraform (Azure provider) or ARM templates Experience with Azure DevOps or GitHub Actions for CI/CD of models and services Knowledge of Azure OpenAI, Azure ML, or equivalent cloud LLM endpoints Familiar with setting up monitoring: Azure Monitor, Prometheus/Grafana—track latency, errors, drift, costs Cost-optimization tactics: spot nodes, autoscaling, GPU utilization tracking Basic LLM understanding: inference latency/cost, deployment patterns, model versioning Ability to build lightweight QA checks or integrate with QA pipelines Cloud-agnostic awareness—experience with AWS or GCP backup systems Comfortable establishing production-grade Ops pipelines, automating deployments end-to-end Self-starter mentality: no playbooks required, ability to pick up new tools and drive infrastructure independently

Company Description We suggest you enter details here. Role Description This is a full-time remote role for a Frontend React JS Developer at Serenovolante Software Services Private Limited. The Frontend React JS Developer will be responsible for creating and implementing user interface components using React.js concepts and workflows such as Redux. The developer will also be responsible for optimizing components for maximum performance across a vast array of web-capable devices and browsers, troubleshooting interface software, collaborating with back-end developers, and participating in all phases of the software development life cycle. Qualifications \n Proficiency in Front-End Development and JavaScript Experience with Redux.js Basic understanding of Back-End Web Development General Software Development skills Strong problem-solving abilities and analytical skills Excellent collaborative skills and team-player attitude Ability to work independently and remotely Bachelor's degree in Computer Science, Information Technology, or a related field

**Who you are** You’ve stepped beyond traditional QA—you test AI agents, not just UI clicks. You build automated tests that check for **hallucinations, bias, adversarial inputs**, prompt chain integrity, model outputs, and multi-agent orchestration failures. You script Python tests and use Postman/Selenium/Playwright for UI/API, and JMeter or k6 for load. You understand vector databases and can test embedding correctness and data flows. You can ask, “What happens when two agents clash?” or “If one agent hijacks context, does the system fail?” and then write tests for these edge cases. You’re cloud-savvy—Azure or AWS—and integrate tests into CI/CD. You debug failures in agent-manager systems and help triage model logic vs infra issues. You take ownership of AI test quality end-to-end. --- **What you’ll actually do** You’ll design **component & end-to-end tests** for multi-agent GenAI workflows (e.g., planner + execution + reporting agents). You’ll script pytest + Postman + Playwright suites that test API functionality, failover logic, agent coordination, and prompt chaining. You’ll simulate coordination failures, misalignment, hallucinations in agent dialogues. You’ll run load tests on LLM endpoints, track latency and cost. You’ll validate that vector DB pipelines (Milvus/FAISS/Pinecone) return accurate embeddings and retrieval results. You’ll build CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins) that gate merges based on model quality thresholds. You’ll implement drift, bias, hallucination metrics, and create dashboards for QA monitoring. You’ll occasion a human-in-the-loop sanity check for critical agent behavior. You’ll write guides so others understand how to test GenAI pipelines. --- **Skills and knowledge** • Python automation—pytest/unittest for component & agent testing • Postman/Newman, Selenium/Playwright/Cypress for UI/API test flows • Load/performance tools—JMeter, k6 for inference endpoints • SQL/NoSQL and data validation for vector DB pipelines • Vector DB testing—Milvus, FAISS, Pinecone embeddings/retrieval accuracy • GenAI evaluation—hallucinations, bias/fairness, embedding similarity (BLEU, ROUGE), adversarial/prompt injection testing • Multi-agent testing—understand component/unit tests per agent, inter-agent communications, coordination failure tests, message passing or blackboard rhythm, emergent behavior monitoring • CI/CD integration—Azure DevOps/GitHub Actions/Jenkins pipelines, gating on quality metrics • Cloud awareness—testing in Azure/AWS/GCP, GenAI endpoints orchestration and failure mode testing • Monitoring & observability—drift, latency, hallucination rate dashboards • Soft traits—detail oriented, QA mindset, self-driven, cross-functional communicator, ethical awareness around AI failures.