Senior Applied Scientist - Agents

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 reasoning and decision systems using planning algorithms, search methods, and optimization techniques, rather than chatbot or RAG-style application development. The ideal candidate will design intelligent agent architectures that combine LLM-based reasoning with classical planning, search algorithms, and optimization techniques, operating 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 also 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

• 3-10 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.
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Candidates whose primary experience is limited to RAG pipelines, prompt engineering, or chatbot frameworks without deeper algorithmic or systems work may not be a fit for this role.

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.

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

We are building an advanced, AI-driven multi-agent software system designed to revolutionize task automation and code generation. This is a futuristic AI platform capable of:

✅ Real-time self-coding based on tasks  

✅ Autonomous multi-agent collaboration  

✅ AI-powered decision-making  

✅ Cross-platform compatibility (Desktop, Web, Mobile)  

We are hiring a highly skilled **AI Engineer & Full-Stack Developer** based in India, with a strong background in AI/ML, multi-agent architecture, and scalable, production-grade software development.

### Responsibilities:

- Build and maintain a multi-agent AI system (AutoGPT, BabyAGI, MetaGPT concepts)  

- Integrate large language models (GPT-4o, Claude, open-source LLMs)  

- Develop full-stack components (Backend: Python, FastAPI/Flask, Frontend: React/Next.js)  

- Work on real-time task execution pipelines  

- Build cross-platform apps using Electron or Flutter  

- Implement Redis, Vector databases, scalable APIs  

- Guide the architecture of autonomous, self-coding AI systems  

### Must-Have Skills:

- Python (advanced, AI applications)  

- AI/ML experience, including multi-agent orchestration  

- LLM integration knowledge  

- Full-stack development: React or Next.js  

- Redis, Vector Databases (e.g., Pinecone, FAISS)  

- Real-time applications (websockets, event-driven)  

- Cloud deployment (AWS, GCP)  

### Good to Have:

- Experience with code-generation AI models (Codex, GPT-4o coding abilities)  

- Microservices and secure system design  

- Knowledge of AI for workflow automation and productivity tools  

Join us to work on cutting-edge AI technology that builds the future of autonomous software.

🚨 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

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. 

Position 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.

Role Overview

We are looking for an experienced Data Scientist – Agentic AI with strong expertise in Python, Machine Learning, Generative AI, Large Language Models (LLMs), RAG and Agentic AI.

The ideal candidate should have hands-on experience in developing, fine-tuning, evaluating and deploying machine learning and GenAI solutions. The candidate should be comfortable working with open-source LLMs, LangChain/LangGraph, PySpark and AI observability/tracing frameworks.

The role involves building intelligent AI systems that can reason, use tools, retrieve information and execute multi-step tasks using Agentic AI architectures.

Mandatory Technical Skills

1. Data Science & Python

• 5+ years of experience in Data Science / Machine Learning / AI.
• Strong programming experience in Python.
• Strong understanding of data analysis, feature engineering and statistical techniques.
• Experience with Python ML and data science libraries such as:
• NumPy
• Pandas
• Scikit-learn
• Matplotlib / Seaborn
• Good understanding of data preprocessing, exploratory data analysis and experimentation.

2. Machine Learning & Statistics

• Strong understanding of Machine Learning fundamentals.
• Experience with supervised and unsupervised learning techniques.
• Knowledge of:
• Regression
• Classification
• Clustering
• Feature Engineering
• Model Selection
• Hyperparameter Tuning
• Cross-validation
• Strong understanding of Statistics / ML fundamentals.
• Ability to interpret model performance and statistical results.

3. Generative AI / LLM

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Understanding of Transformer architecture and modern LLM-based applications.
• Experience working with commercial or open-source LLMs.
• Strong understanding of:
• Prompt Engineering
• Context Management
• Embeddings
• Tokenization
• LLM inference
• Hallucination mitigation

4. RAG – Retrieval Augmented Generation

• Strong hands-on experience developing RAG applications.
• Experience with:
• Document ingestion
• Chunking
• Embeddings
• Vector search
• Semantic search
• Retrieval pipelines
• Context retrieval
• Reranking
• Ability to optimize RAG pipelines for relevance, accuracy and latency.
• Experience integrating LLMs with enterprise knowledge sources.

5. Agentic AI

• Hands-on experience building Agentic AI / AI Agent solutions.
• Understanding of agent architecture and multi-step reasoning workflows.
• Experience with:
• AI Agents
• Multi-Agent systems
• Tool Calling
• Function Calling
• Agent orchestration
• Planning and reasoning workflows
• Memory
• Workflow automation
• Ability to build agents that can interact with tools, APIs, databases and external systems.

6. LangChain / LangGraph

• Strong hands-on experience with LangChain and/or LangGraph.
• Experience building LLM workflows and agent-based applications.
• Understanding of:
• Chains
• Agents
• Tools
• State management
• Graph-based workflows
• Agent orchestration
• Retrieval workflows
• Experience designing scalable Agentic AI workflows.

7. LLM Fine-Tuning

• Hands-on experience with LLM fine-tuning.
• Understanding of techniques such as:
• Supervised Fine-Tuning (SFT)
• Parameter-Efficient Fine-Tuning
• LoRA
• QLoRA
• Experience preparing datasets for fine-tuning.
• Ability to evaluate fine-tuned models against baseline models.
• Understanding of model optimization and inference considerations.

8. BERT / LLaMA / Open-Source LLMs

Experience working with one or more open-source / transformer-based models such as:

• BERT
• LLaMA / Llama
• Mistral
• Gemma
• Qwen
• Other open-source LLMs

Candidate should understand model loading, inference, fine-tuning and evaluation.

9. PySpark

• Strong experience with PySpark for large-scale data processing.
• Experience working with large datasets and distributed data processing.
• Knowledge of:
• Data transformations
• Data cleaning
• Aggregations
• Joins
• Spark SQL
• Performance optimization
• Ability to build scalable data processing pipelines.

10. Model Validation & Evaluation

• Experience validating and evaluating ML and GenAI models.
• Understanding of traditional ML evaluation metrics.
• Experience evaluating LLM/RAG applications using relevant quality metrics.
• Ability to compare model performance and identify areas for improvement.
• Experience with:
• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC
• Retrieval metrics
• LLM response quality
• Groundedness / relevance
• Experience designing evaluation datasets and test cases is preferred.

11. AI Tracing / Observability

• Experience with AI/LLM tracing and observability.
• Ability to monitor AI applications in production.
• Experience tracking:
• LLM requests/responses
• Latency
• Token usage
• Errors
• Retrieval performance
• Agent/tool execution
• Model performance
• Exposure to tools/frameworks such as LangSmith, OpenTelemetry, Arize Phoenix, MLflow or similar is preferred.

12. Model Deployment

• Experience deploying ML/LLM/GenAI solutions into production.
• Exposure to cloud and/or on-premise model deployment.
• Experience with model serving, APIs and production inference.
• Knowledge of deployment environments such as:
• AWS
• Azure
• GCP
• On-premise infrastructure
• Experience with Docker, APIs and CI/CD is an advantage.

Key Responsibilities

• Design, develop and deploy Data Science, Machine Learning and GenAI solutions.
• Build production-ready RAG and Agentic AI applications.
• Develop intelligent agents capable of tool calling, reasoning and multi-step task execution.
• Build LLM-powered applications using LangChain/LangGraph.
• Work with open-source LLMs including BERT, LLaMA and other transformer-based models.
• Fine-tune LLMs for specific business use cases.
• Develop scalable data processing pipelines using PySpark.
• Perform data analysis, feature engineering and statistical modeling.
• Develop and maintain model validation and evaluation frameworks.
• Evaluate ML and LLM models using appropriate performance and quality metrics.
• Implement AI tracing, monitoring and observability for production GenAI systems.
• Deploy models and AI applications in cloud or on-premise environments.
• Optimize model performance, response quality, latency and cost.
• Troubleshoot issues related to model inference, retrieval, agents and LLM workflows.
• Collaborate with Data Scientists, ML Engineers, Software Engineers and business stakeholders.
• Convert business requirements into scalable AI/ML solutions.

Good to Have

• Experience with Vector Databases such as:
• FAISS
• Pinecone
• Weaviate
• Milvus
• Chroma
• Azure AI Search
• Experience with MLflow or similar ML lifecycle tools.
• Experience with Docker/Kubernetes.
• Experience with REST APIs / FastAPI.
• Knowledge of cloud AI/ML services.
• Experience with MLOps / LLMOps.
• Experience with multi-agent frameworks other than LangChain/LangGraph.
• Experience working with enterprise GenAI applications.

Ideal Candidate Profile

The ideal candidate should be a Data Scientist / ML Engineer with strong GenAI and Agentic AI experience, rather than a pure Python developer.

A strong candidate would typically have:

Data Science + Python + ML + Statistics + GenAI/LLM + RAG + Agentic AI + LangChain/LangGraph + LLM Fine-Tuning + Open-Source LLMs + PySpark + Model Evaluation + AI Observability + Model Deployment.

Core Mandatory Skills

Data Science, Python, Machine Learning, Statistics/ML Fundamentals, GenAI/LLM, RAG, Agentic AI, LangChain/LangGraph, LLM Fine-Tuning, BERT/LLaMA/Open-Source LLMs, PySpark, Model Validation/Evaluation, AI Tracing/Observability, Cloud/On-Prem Model Deployment.

About us

MyRico builds personal AI agents for enterprise, the copilots and digital employees that make humans more productive. The MyRico agents sit at the intersection of enterprise memory, high-end security, and an ever-expanding set of capabilities. We're a small team shipping fast, and the product is live with real customers today.

The role

Full-time · Bangalore

You'll own the systems that make an autonomous agent trustworthy in production. This is not just prompt engineering, and it's not model training - it's that and all the engineering layers in between: model steering, memory architecture, orchestration design, latency optimization, deterministic vs non-deterministic systems tradeoff, deployment, and operator tooling.

Concretely, the kind of work you'd have done here last week would include enhancing agent memory systems, runtime and scheduling reliability and predictability, adding new capability and tools to deployed agents, operator tools, live system debugging and benchmarking various models for price, latency and quality. And that is just last week. We are a small nimble startup rapidly working to address customer needs in this growing space, so things change rapidly.

What we're looking for

• More than 7 years of software engineering, with real production ownership of distributed or stateful systems - you've been paged for something you built and made it not happen again.
• Strong understanding of LLM based native app building, combining classic and model driven applications to get the best of both. You've built on LLMs beyond demos: agent frameworks, tool use, context management, eval fixtures, and you know why "it worked in the transcript" isn't evidence.
• Python and shell in production settings; comfortable in TypeScript/Node. You write boring, testable code and prefer the standard library to a new dependency.
• Systems taste: append-only logs, idempotent reconciliation, fold-the-events state machines, and read-only debugging surfaces feel like home.
• Evidence discipline: tests before features, claims backed by quoted observations, decisions written down.

Nice to have

• Experience running the combination of multi-tenant and single-tenant / on-prem-style fleets with ability to handle per-customer isolation, upgrade paths, migration compatibility in both setups.
• Security instincts for products that touch highly sensitive data and systems, including things like executives' email, calendars, and messages: least privilege, loopback-only services, secrets that never hit a log.
• You already orchestrate AI coding agents in your own workflow and have opinions about where they break.

How we work

Small team, high trust, written decisions. Designs get adversarial review before code; PRs get automated review driven to zero open findings; features aren't done until verified on a live system. AI agents do a large share of the implementation, your leverage is judgment: framing the problem, freezing the right design, and knowing when the machine is wrong.

Product Engineer — Role Summary

We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.

Key Responsibilities:

• Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
• Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
• Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
• Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
• AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
• User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
• Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
• Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.

The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.

Job Description – AI Engineer (End-to-End Development & Deployment)

Role Summary

We are looking for an AI Engineer with hands-on experience in designing, developing, deploying, and maintaining Generative/Agentic AI solutions in production. The ideal candidate should have end-to-end ownership of AI applications, from development to deployment, monitoring, and optimization.

Key Responsibilities

●        Design, build, and deploy Generative/Agentic AI solutions.

●        Develop applications using LLMs, RAG, AI agents, and vector databases.

●        Build scalable APIs and integrate AI solutions with enterprise applications.

●        Implement CI/CD pipelines, containerization, and MLOps best practices.

●        Monitor, optimize, and maintain production AI systems.

●        Collaborate with cross-functional teams to deliver business-driven AI solutions.

Required Skills

●       Strong programming skills in Python.

●       Experience with vector databases (e.g., Pinecone, FAISS, ChromaDB) and graph memory systems

●       Knowledge of atleast one agent development framework: Google ADK (preferred), LangChain/LangGraph/LlamaIndex, CrewAI

●       Experience with LLMs, RAG, GenAI, AgenticAI Agents

●       Hands-on experience with FastAPI, and REST APIs.

●       Knowledge of Docker, Kubernetes, Git, CI/CD.

●       Experience with AWS, Azure, or GCP. 

●       Experience with security compliance, monitoring and observability tools such as AWS CloudWatch, Azure Monitor, Google Cloud Monitoring.