Senior Applied Scientist - Agents at Auxo AI · Bengaluru (Bangalore), Mumbai, Delhi, Gurugram, Hyderabad · 2 - 5 years · ₹20L - ₹35L / yr (ESOP available) · Raised funding · Posted 9 Mar 2026
AuxoAI is hiring a Senior Applied Scientist to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.
This role focuses on building intelligent agent systems that combine LLM-based reasoning with classical planning, search algorithms, and optimization techniques. The ideal candidate will develop robust agent architectures that operate reliably in real-world environments with constraints around latency, cost, uncertainty, and limited context windows.
You will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems.
You will work on problems where existing architectures may not be sufficient, and will be expected to experiment with new approaches that combine machine learning, graph algorithms, and classical AI techniques to build reliable, production-grade systems.
Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)
Responsibilities:
- Design and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.
- Implement planning and search algorithms such as Monte Carlo Tree Search (MCTS), beam search, A search, heuristic search, and graph-based planning approaches* to support complex decision-making tasks.
- Develop decision-making loops that balance trade-offs between exploration vs. exploitation, cost vs. accuracy, and latency vs. reasoning depth.
- Build structured memory systems including episodic memory stores, semantic memory layers, and vector-based memory with optimized retrieval strategies.
- Design tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.
- Develop evaluation frameworks to measure agent performance using task success metrics, rollout simulations, and multi-sample validation approaches.
- Improve agent performance through techniques such as distillation, synthetic trajectory generation, prompt compression, and context pruning.
- Deliver production-ready agent systems that meet operational requirements around reliability, cost efficiency, throughput, and observability.
Requirements
- 2-5 years of experience building machine learning or AI systems in production environments.
- Strong experience implementing search or planning algorithms beyond basic use cases, including tree search or heuristic-based planning approaches.
- Hands-on experience with Monte Carlo Tree Search (MCTS) or related decision-making frameworks.
- Strong understanding of state-space representations, heuristic design, and decision boundary trade-offs.
- Experience building or extensively customizing agent frameworks for real-world applications.
- Hands-on experience designing tool-use or function-calling architectures under practical system constraints.
- Strong Python engineering skills with a focus on scalable and reliable system design.
Nice to Have:
- Experience with reinforcement learning techniques such as policy gradients, value estimation, or reward modeling.
- Experience building multi-agent or collaborative agent systems.
- Experience designing evaluation frameworks for agent robustness and reliability.
- Experience optimizing LLM inference pipelines for latency, throughput, and cost efficiency.
- Familiarity with distributed task orchestration systems and large-scale AI workflow management.
Shubham Vishwakarma
Full Stack Developer - Averlon
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PythonApplied AI Engineer
About the Role
We are building an AI-powered platform that transforms credit origination by automating manual processes and enabling lenders to underwrite more loans with greater accuracy and lower risk.
We are looking for an experienced Applied AI Engineer to design and build production-grade Agentic AI systems capable of complex, multi-step reasoning across financial data, legal documents, and other unstructured information.
As an early engineering team member, you will take ownership of core AI architecture, multi-agent orchestration, reasoning workflows, evaluation frameworks, and scalable AI infrastructure.
Key Responsibilities
- Design and develop stateful, multi-agent AI systems for complex reasoning and decision-making.
- Build and optimize LLM pipelines using frameworks such as LangChain, DSPy, and GEPA.
- Implement RAG, prompt optimization, memory management, tool integration, and human-in-the-loop workflows.
- Develop AI evaluation frameworks using LLM-as-a-Judge and scenario-based testing.
- Implement AI observability and monitoring to track accuracy, latency, cost, and system performance.
- Build ETL pipelines and work with structured and unstructured data, SQL/NoSQL databases, vector databases, and graph-based data models.
- Integrate external tools and systems using modern agent protocols such as Model Context Protocol (MCP).
- Deploy and maintain AI services on AWS or Azure.
- Implement CI/CD pipelines, containerization, and Infrastructure as Code for reliable production deployments.
Required Qualifications
- 5+ years of professional software development experience in Python or JavaScript.
- Proven experience building and deploying production-level Agentic AI systems or complex LLM reasoning applications.
- Hands-on experience with LangChain and LLMOps, including tools such as Langfuse, Weights & Biases (W&B), or Helicone.
- Strong understanding of LLMs, RAG, prompt engineering, multi-agent orchestration, memory management, and tool calling.
- Experience with DSPy, GEPA, or advanced LLM inference and optimization techniques.
- Strong data engineering fundamentals, including ETL, SQL/NoSQL databases, and vector databases.
- Familiarity with MCP and human-in-the-loop workflows.
- Experience with AWS or Azure and modern DevOps practices, including Docker, CI/CD, and Infrastructure as Code.
Good to Have
- Experience with RLHF or RLAIF.
- Knowledge of model fine-tuning techniques such as LoRA and QLoRA.
- Experience with graph databases and graph-based data models.
- Exposure to financial services, credit underwriting, risk analysis, or document-intensive AI applications.
Why Join Us?
- Work on advanced Agentic AI and complex reasoning systems beyond conventional chatbot applications.
- Build foundational AI architecture and production-grade LLM workflows.
- Take ownership of technical decisions and system design as an early engineering team member.
- Solve challenging problems at the intersection of AI, finance, and intelligent automation.
Data SciencePython🚨 Hiring – Data Scientist | Python + Agentic AI
💼 Experience: 5+ Years
Must Have:
• Strong Data Science experience
• Python
• Agentic AI / AI Agents
• Generative AI / LLMs
• RAG / Vector Databases
• LangChain / LangGraph or similar Agent Frameworks
• Machine Learning & NLP
PythonHiring AI Engineer
Exp : 5 - 8 yrs
Edu : BE/B.Tech/MCA
Work Location : Pune WFO
Skills :
Min 4 years strong programming experience in Python is a MUST.
Min 2 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks
RAG with deep retrieval experience (most important):
Owned a RAG system end to end in production, not a demo.
Has built an MCP server or client, or built agents using LangGraph/Semantic Kernel (tool calling, multi-step workflows).
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5
Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.
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Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale
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Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)
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Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).
10
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Flask
DjangoJob Title: Senior AI Engineer
Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred
Employment Type: Full-time
Department: Artificial Intelligence / Engineering
Location: Bangalore
We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.
The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.
Key Responsibilities
1. AI Solution Design and Development
- Design, develop, and deploy end-to-end AI and machine learning solutions.
- Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
- Develop scalable AI architectures and reusable components for enterprise applications.
- Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.
2. Generative AI and LLM Engineering
- Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
- Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
- Build AI agents and multi-step workflows using appropriate orchestration frameworks.
- Implement prompt engineering, structured outputs, tool calling, and context management.
- Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.
3. Machine Learning and Data Engineering
- Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
- Work with structured, unstructured, and multimodal data.
- Build data preprocessing, feature engineering, model training, and validation pipelines.
- Apply appropriate model evaluation techniques and statistical methods.
4. Production Deployment and MLOps / LLMOps
- Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- Build automated CI/CD pipelines for AI applications and model deployment.
- Implement model versioning, experiment tracking, monitoring, and performance evaluation.
- Optimize inference latency, throughput, infrastructure utilization, and operating costs.
- Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.
5. Software Engineering and System Architecture
- Develop production-quality applications and APIs using Python and modern backend frameworks.
- Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
- Integrate AI services with enterprise applications, databases, and third-party systems.
- Use relational databases, vector databases, caching, and distributed systems effectively.
- Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.
6. Security, Quality, and Responsible AI
- Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
- Apply appropriate privacy, security, and compliance controls to AI systems.
- Evaluate hallucinations, bias, reliability, and failure modes.
- Design human-in-the-loop workflows and fallback mechanisms for critical use cases.
7. Technical Leadership and Collaboration
- Lead technical design discussions and mentor junior AI engineers.
- Define engineering standards, coding practices, and AI development guidelines.
- Collaborate with cross-functional teams to deliver measurable business outcomes.
- Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
- Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.
Required Technical Skills
Programming and Backend Development
- Strong proficiency in Python.
- Experience with FastAPI, Flask, or Django.
- Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
- Experience with Git, automated testing, and CI/CD.
AI and Machine Learning
- Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
- Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
- Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.
Generative AI and LLMs
- Hands-on experience building production applications using LLM APIs or open-source models.
- Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
- Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
- Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
- Experience with tool calling, structured generation, agent workflows, and LLM evaluation.
Cloud and Infrastructure
- Experience with AWS, Azure, or Google Cloud.
- Working knowledge of Docker and Kubernetes.
- Familiarity with model serving, scalable APIs, monitoring, and production deployments.
- Experience with MLOps or LLMOps practices.
Databases and Data Processing
- Strong SQL skills and experience with relational databases.
- Familiarity with NoSQL databases, data pipelines, and distributed processing.
- Experience working with unstructured data, document ingestion, and retrieval systems.
Preferred Qualifications
- Experience with fine-tuning, quantization, or serving open-source LLMs.
- Experience with multimodal AI, including text, image, audio, or video.
- Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
- Experience with AI security, guardrails, governance, and responsible AI.
- Experience optimizing AI workloads for latency, scalability, and cost.
- Contributions to open-source AI projects, research, or technical publications.
Education
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
- Equivalent practical experience and demonstrated engineering expertise may be considered.
Key Success Metrics
- Quality, accuracy, and reliability of deployed AI solutions.
- Production adoption and measurable business impact.
- Reduction in inference costs and response latency.
- RAG retrieval quality and reduction in unsupported or incorrect responses.
- System availability, scalability, and operational stability.
- Delivery timelines, code quality, and technical mentoring effectiveness.
Strong Lead AI Engineer Profile with agentic systems architecture expertise
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Mandatory (Experience 1): Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems
3
Mandatory (Tech skill 1): Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals
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5
Mandatory (Tech skill 3): Must have Python mastery — production-quality code, type annotations, unit and integration tests, packaging, and performance profiling
6
Mandatory (Tech skill 4): Must have RAG pipeline depth — embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses
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Mandatory (Tech skill 5): Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics (Terraform or CDK), and CI/CD pipelines
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Mandatory (Tech skill 6): Must have agent observability experience — LangSmith, Helicone, or equivalent — with ability to diagnose latency, cost, and quality issues in production traces
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Mandatory (Tech skill 8) : Must have owned full sub-system designs — agent topology, data flows, API contracts, failure handling — for a bounded scope, and built multi-agent graphs, tool orchestration, retrieval, and memory systems.
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Mandatory (Tech skill 7): Must have track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks
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Mandatory (Communication): Must be able to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders
Strong Lead AI Engineer Profile with agentic systems architecture expertise
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Mandatory (Experience 1): Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems
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Mandatory (Tech skill 1): Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals
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Mandatory (Tech skill 2): Must have direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder
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Mandatory (Tech skill 3): Must have Python mastery — production-quality code, type annotations, unit and integration tests, packaging, and performance profiling
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Mandatory (Tech skill 4): Must have RAG pipeline depth — embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses
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Mandatory (Tech skill 5): Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics (Terraform or CDK), and CI/CD pipelines
8
Mandatory (Tech skill 6): Must have agent observability experience — LangSmith, Helicone, or equivalent — with ability to diagnose latency, cost, and quality issues in production traces
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Mandatory (Tech skill 8) : Must have owned full sub-system designs — agent topology, data flows, API contracts, failure handling — for a bounded scope, and built multi-agent graphs, tool orchestration, retrieval, and memory systems.
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Mandatory (Tech skill 7): Must have track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks
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Mandatory (Note 1) : Role is Hybrid, WFH flexibility as well upto 6 days a month
Role Overview
We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale.
Key Responsibilities
Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production.
Write clean, tested, production-grade code and participate actively in code and design reviews.
Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production.
Required Qualifications
8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs.
Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel).
Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers.
Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability.
PythonAI Engineer
Focus: Agentic AI, AI Shopping Assistants & Virtual Try-On
Role Summary :
We are looking for a Full-Stack AI Engineer to build, deploy, and operate production-grade AI applications, including agentic shopping assistants and multimodal AI experiences. The role requires strong hands-on expertise in Python, TypeScript, LLM-based applications, real-time streaming, evaluation, and cloud infrastructure.
The ideal candidate can independently deliver end-to-end features across backend agent runtimes and frontend interfaces, with a strong focus on reliability, latency, cost optimisation, and production performance.
Key Responsibilities :
- AI Agent Development: Build and maintain Python-based agent runtimes using LLMs, tool calling, structured outputs, retrieval-augmented generation (RAG), multi-turn state, and human escalation workflows.
- Full-Stack Engineering: Develop asynchronous Python APIs and TypeScript web components, implementing server-sent events (SSE), streaming interfaces, interactive product cards, and resilient client-side error handling.
- LLM Evaluation: Build evaluation datasets and automated test harnesses using deterministic and model-based graders, regression gates, ablation studies, and pass@k/pass^k metrics.
- Performance & Cost Optimisation: Improve response latency, time to first token, token consumption, caching efficiency, context management, and cost per conversation.
- Production Operations: Implement distributed tracing, structured logging, LLM observability, monitoring, incident resolution, feature flags, controlled rollouts, and rollback mechanisms.
- Cloud & DevOps: Deploy containerised services, manage CI/CD pipelines, infrastructure as code, and production workloads on cloud platforms.
- E-commerce AI Integration: Integrate product search, catalogue data, product details, grounded recommendations, and commerce-related event tracking into AI-powered shopping experiences.
- Virtual Try-On & Multimodal AI: Contribute to image-based generative AI applications, including embeddable frontend widgets and scalable backend services.
- Internationalisation & Scalability: Support multilingual experiences, brand-specific configurations, localisation, currency handling, and backward-compatible integrations.
- Cross-Functional Collaboration: Work with product, business, and engineering stakeholders to troubleshoot quality issues and deliver measurable improvements to customer experience and business outcomes.
Essential Skills & Experience :
- Strong hands-on experience with Python and TypeScript.
- Production experience building LLM-powered applications using tool/function calling, structured outputs, streaming, retries, context management, and RAG.
- Strong understanding of asynchronous Python, REST APIs, SSE, and event-driven communication.
- Practical experience designing LLM evaluation frameworks, test datasets, automated graders, regression testing, and evaluation metrics.
- Experience deploying and operating cloud-based applications, containerised services, CI/CD pipelines, and infrastructure as code.
- Familiarity with production observability, distributed tracing, structured logging, performance monitoring, and incident management.
- Experience with caching, latency optimisation, token and inference cost management, feature flags, and safe deployment strategies.
- Strong software engineering fundamentals, debugging skills, and end-to-end ownership of production features.
Preferred Skills :
- Experience with agent frameworks such as Google ADK, LangGraph, or Claude Agent SDK.
- Hands-on exposure to Google Cloud Platform, Vertex AI, Gemini, FastAPI, and Cloud Run.
- Experience with frontend frameworks and tools such as Lit, Next.js, esbuild, and Vitest.
- Familiarity with PostgreSQL, BigQuery, OpenTelemetry, Opik, and LLM tracing platforms.
- Knowledge of MCP servers, sub-agents, agent-to-agent integrations, and retrieval systems.
- Experience with e-commerce search, multimodal AI, image generation, virtual try-on, and third-party web embeds.
- Understanding of prompt-injection mitigation, multilingual applications, and inference performance optimisation.
- Effective use of AI coding assistants and developer productivity tools.
Role Expectations :
- Deliver production-ready features across both backend AI services and frontend applications.
- Demonstrate evaluation evidence, observability, and safe rollout practices for AI-driven changes.
- Balance response quality, reliability, latency, infrastructure costs, and user experience.
- Work effectively within existing systems, maintaining backward compatibility and planning migrations and rollbacks.
- Take ownership of troubleshooting, continuous improvement, and production reliability.
Python
C#
React.jsPosition Overview
We are seeking a versatile Senior Full Stack & AI Agent Developer to architect, build, and
maintain end-to-end software solutions spanning web platforms, desktop applications, and
autonomous AI agents capable of interacting with and controlling these software systems.
The ideal candidate will bridge traditional engineering software with cutting-edge artificial
intelligence to automate data processing and enhance operational decision-making. While
not strictly required, a background or strong interest in the energy sector—specifically
drilling and completion operations—is highly desirable.
Key Responsibilities
• Full Stack Development: Design, develop, and deploy robust web applications and
native desktop software utilized by engineering and operational teams.
• AI Agent Engineering: Build, train, and integrate autonomous AI agents and LLM-
driven workflows capable of interpreting data, executing commands, and safely
controlling desktop and web-based software.
• Workflow Automation: Translate complex workflows into intuitive software features
and autonomous agent actions, minimizing manual data entry and operational
bottlenecks.
• Data Integration: Handle high-frequency data streams and integrate them seamlessly
into user interfaces and backend AI models.
• Architecture & Scalability: Ensure high performance, security, and scalability across
cloud infrastructure (AWS/Azure), local desktop environments, and potential edge
computing setups.
• Cross-Functional Collaboration: Work closely with domain experts and end-users to
translate field challenges into technical product requirements.
Required Qualifications & Experience
• Experience: Minimum of 5 years of professional software development experience,
with a proven track record of delivering production-ready web and desktop
applications.
• Programming Languages: Strong proficiency in Python, JavaScript/TypeScript, and at
least one compiled language (C#, C++, or Java).• Web & Desktop Frameworks: Hands-on experience with modern frontend
frameworks (React, Angular, or Vue.js), Node.js, and desktop application development
(Electron, WPF, Qt, or Tauri).
• AI & Agent Tooling: Demonstrated experience building AI agents using LLM APIs
(OpenAI, Anthropic), open-source models (Hugging Face), LangChain, LlamaIndex,
AutoGPT, or custom agent architectures.
• Automation & UI Control: Expertise in software control mechanisms using tools like
Selenium, Playwright, PyAutoGUI, Appium, or computer vision-based GUI automation to
allow AI agents to navigate software.
• Cloud, DevOps & Databases: Experience with Git, Docker, CI/CD pipelines, cloud
platforms (AWS/Azure/GCP), RESTful APIs, GraphQL, and relational/NoSQL databases.
Preferred Qualifications (Strong Plus)
• Industry Domain Expertise: Prior hands-on development experience within the oil and
gas sector, specifically focused on drilling, completions, rig operations, or subsurface
engineering software.
• Data & Protocols: Familiarity with oilfield data standards (e.g., WITSML, OPC-UA) and
time-series databases.
• Experience deploying AI models and agents in edge or low-connectivity environments
(such as offshore rigs or remote drilling sites).
• Familiarity with safety-critical software design and cybersecurity standards in
industrial control systems (ICS/SCADA).
• Degree in Computer Science, Software Engineering, Petroleum Engineering, or a related technical discipline.
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon