AI Engineer

Role Overview

We are looking for an experienced AI/ML Engineer with strong expertise in Python, Generative AI, LLMs, LangChain, and LangGraph. The candidate will be responsible for designing and developing AI-powered applications, intelligent agents, and scalable LLM-based solutions.

Key Responsibilities

• Design and develop AI/ML and Generative AI applications using Python and modern LLM technologies.
• Build LLM-based applications and AI agents using LangChain and LangGraph.
• Develop agentic workflows involving tool calling, memory, reasoning, and multi-step orchestration.
• Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or other foundation models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases.
• Work with embeddings, prompt engineering, semantic search, and document processing.
• Develop scalable APIs and backend services using Python, FastAPI, or Flask.
• Build and integrate AI solutions with existing applications and enterprise systems.
• Deploy and maintain AI/ML solutions on cloud platforms.
• Collaborate with data scientists, software engineers, and product teams to develop business-focused AI solutions.

Required Skills

• Strong hands-on experience in Python programming.
• Strong experience in AI/ML and Generative AI.
• Hands-on experience with LLMs and LLM-based application development.
• Strong experience with LangChain and/or LangGraph.
• Experience building AI Agents / Agentic AI workflows.
• Strong understanding of RAG, embeddings, vector databases, and prompt engineering.
• Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, or Azure AI Search.
• Experience developing REST APIs using FastAPI/Flask.
• Good understanding of Machine Learning, NLP, and deep learning concepts.
• Experience with Azure / AWS / GCP cloud platforms.

Good to Have

• Experience with multi-agent systems and agent orchestration.
• Knowledge of MLOps / LLMOps.
• Experience with Docker, Kubernetes, and CI/CD.
• Knowledge of LLM evaluation, monitoring, observability, and AI governance.
• Experience with Azure OpenAI, Azure AI Foundry, or AWS Bedrock.

Applied 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.

• Primary Skillsets: Python, Generative AI, Agentic AI, LLM, experience with designing and developing production grade AI solutions, AWS/Azure/GCP, LLMOps, Data Engineering (Databricks/Snowflake), Regulated GenAI, Data and Decisions, and Agentic Systems.
• Preferred: Pharma or life sciences experience, especially in regulated (GxP) environments, experience setting up a team or practice from scratch.
• Only immediate joiners are considered.

About the Role

We are looking for an AI/ML Engineer with 4+ years of Strong hands-on experience building and deploying production-grade AI systems. The ideal candidate should have strong expertise in Computer Vision, Generative AI, RAG pipelines, Voice AI, and LLM-powered applications, with experience delivering scalable, production-ready solutions across enterprise, healthcare, or SaaS environments

Key Responsibilities

• Design, develop, and deploy production-ready AI/ML and Generative AI applications, Computer Vision solutions.
• Build and optimize LLM-powered applications for real-world business use cases.
• Develop and maintain RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models.
• Build Voice AI solutions including speech-to-text (STT), text-to-speech (TTS), conversational AI, and voice-enabled assistants.
• Create and optimize prompt engineering strategies to improve LLM output quality, consistency, and reliability.
• Work with open-source LLMs for inference, fine-tuning, evaluation, and deployment.
• Implement model monitoring, evaluation, guardrails, and performance optimization.
• Collaborate with DevOps and cloud teams for scalable deployments.
• Optimize AI systems for latency, cost, scalability, and production performance.
• Integrate AI services through APIs and enterprise applications.

Required Skills & Experience

• 4+ years of experience in AI/ML engineering and production deployments.
• Strong experience in Python and AI/ML development.
• Hands-on experience with Generative AI and LLM applications.
• Strong hands-on experience in Computer Vision using Python and frameworks such as OpenCV, PyTorch, and TensorFlow.
• Experience with Computer Vision tasks such as object detection, image classification, image segmentation, and feature extraction.
• Familiarity with models and libraries such as YOLO, Detectron2, MMDetection, and torchvision.
• Experience with image preprocessing, data augmentation, model training, evaluation, and inference optimization.
• Exposure to OCR technologies such as Tesseract or EasyOCR and vision-based deep learning models is an advantage.
• Experience building RAG pipelines using:
• Vector Databases: Pinecone, Weaviate, FAISS, ChromaDB, Milvus
• Embeddings and retrieval optimization
• Experience with Prompt Engineering techniques for LLM optimization.
• Experience with Open Source LLMs such as:
• Llama, Mistral, Falcon, Gemma, DeepSeek, Qwen
• Experience with Voice AI technologies:
• Speech-to-Text (Whisper, Deepgram, Google STT)
• Text-to-Speech (ElevenLabs, Azure Speech, Amazon Polly)
• Conversational Voice Agents
• Experience using frameworks/tools:
• LangChain, LangGraph, LlamaIndex, Hugging Face
• Understanding of fine-tuning, model evaluation, inference optimization, and AI observability.
• Experience deploying applications using:
• Docker, Kubernetes, CI/CD
• AWS / Azure / GCP

Preferred Qualifications

• Experience delivering AI solutions for Healthcare, Enterprise, or SaaS platforms.
• Exposure to MLOps practices and model lifecycle management.
• Understanding of AI governance, security, and responsible AI practices.
• Experience integrating AI with APIs and production services.

Nice to Have

• Knowledge of agentic AI workflows and orchestration.
• Experience with monitoring and evaluation frameworks.
• Exposure to multimodal AI applications

Education Qualifications

• B.Tech / M.Tech in CSE / IT / AI&ML only from good college.

Job Title: Senior AI/ML Engineer

Company: Timble Technologies Pvt. Ltd

Location: Gurugram (Hybrid)

Experience: 2 TO 5 Years

About Us

Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.

Role Overview

We are looking for a hands-on Senior AI/ML Engineer to design, develop, and productionize high-throughput AI/ML and Generative AI systems. You will own the full lifecycle—from problem formulation and data pipelines to deep learning architectures, RAG systems, LLMOps, and model governance—delivering sub-second latency and high reliability across our enterprise products.

Key Responsibilities

·       Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).

·       GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

·       Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

·       MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

·       Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

·       Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

Required Qualifications & Skills

·       Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

·       Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

·       GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

·       Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

·       Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

·       Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

·       Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

·       Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

·       Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

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. 

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.

Immediate Joiner - 15 days Notice period

• Primary Skillsets: Python, Generative AI, Agentic AI, LLM, experience with designing and developing production grade AI solutions, AWS/Azure/GCP, LLMOps, Data Engineering (Databricks/Snowflake), Regulated GenAI, Data and Decisions, and Agentic Systems.

• Preferred: Pharma or life sciences experience, especially in regulated (GxP) environments, experience setting up a team or practice from scratch.

Support with design and build to prove out agentic AI solution flow by working with other data 

scientists and engineers to build, train Large Language Model (LLM) architectures, RAG 

systems, and autonomous agentic workflows 

Key qualifications: 

>> AI solution design & Development: Design Agentic AI solutions using RAG (Retrieval-

Augmented Generation) and orchestration frameworks like LangGraph or LangChain. 

>> Model Fine-Tuning: Solid understanding and experience with Pre-train, fine-tune, and 

optimize open-source like BERT, LLama, and other proprietary foundation models for domain-

specific tasks 

>> Solid Stats and ML foundations and (vibe) coding skills with Python, PySpark 

>>  Implement validation frameworks and tracing practices (using tools like Arize) to monitor 

agent behavior, guard against model drift, and ensure compliance 

>> Collaborate with Engineering to deploy models securely on cloud and on-prem ecosystems 

About the Role

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a Senior AI/ML Backend Engineer to help build the core intelligence layer powering Lumen and Agent Studio. You will design and ship production-grade backend systems that integrate LLMs into real agentic workflows — taking actions, retrieving knowledge and generating insights inside a live CRM product used by real businesses. This is a hands-on, build-focused role with direct ownership of systems that ship to production.

What You’ll Do

• Design, build and scale backend services in Python that power LLM-driven and agentic features within Lumen and Agent Studio.
• Build and productionize agentic AI systems — including planning, tool use, orchestration, memory and multi-step task execution.
• Integrate LLMs into core product workflows, focusing on reliability, latency, cost and correctness at production scale.
• Build robust APIs and services that connect AI agents with CRM data, business logic and third-party systems.
• Own evaluation, testing and monitoring for AI features to ensure they behave reliably in real-world, not just demo, conditions.
• Collaborate closely with product, design and other engineers to take features from zero to one and iterate rapidly based on real usage and customer feedback.
• Work directly with customers and customer-facing teams to understand real workflows, debug issues and translate feedback into product and engineering decisions.

What We’re Looking For

• 2–4 years of professional backend engineering experience, with strong hands-on Python skills.
• Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
• Should be hands-on with traditional Machine learning frameworks like Pytorch, Scikit-learn
• Solid understanding of API design, backend architecture, databases and distributed systems fundamentals.
• Familiarity with LLM orchestration concepts — prompting, tool/function calling, RAG, agent frameworks, evaluation and guardrails.
• Comfort working in a fast-paced, ambiguous, zero-to-one environment where you’ll be defining as much as building.
• Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

• Experience with enterprise security, reliability or observability practices for AI systems.
• Prior experience working on CRM, SaaS or other enterprise business software.
• Exposure to voice AI or real-time systems.