AI/ML Engineer with Strong Backend Skills

Skill Set

Large language,Artificial Intelligence,Machine Learning

- 4–7 years of experience in software engineering/AI roles

- Strong programming skills in Python or TypeScript (Java/Go is a plus)

- Hands-on experience with LLMs, RAG pipelines, and AI frameworks

- Experience building APIs and working with distributed systems

- Familiarity with Kubernetes, Docker, and CI/CD pipelines

- Experience with cloud platforms (AWS/Azure/GCP)

Excellent communication

AI Engineer

LLMs, Agents & AI Services

📍 Mumbai (On-site) | Full-time | 2-4 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.

AI is core to how we design, deliver, and scale software for our customers.

We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.

The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.

The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.

You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.

Responsibilities:

Solutioning and POCs

Translate ambiguous customer problems into working POCs at speed.

Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.

LLM Application Development

Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).

Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.

Agentic System Design

Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.

Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.

API and Service Development

Build production AI services and APIs using Python and FastAPI.

Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.

Retrieval and Tool Integration

Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.

Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.

Cost Analysis and Unit Economics

Model the per-request and per-user cost of every AI feature before it ships.

Track token usage, prompt caching, batching, and model-routing strategies.

Drive measurable improvements in unit economics.

Production Hardening

Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.

Prompt Engineering and Evaluation

Design, test, and iterate prompts with measured outcomes.

Build evaluation harnesses for accuracy, hallucination, latency, and cost.

Run benchmarks across models and prompt variants before locking in a design.

Requirements:

AI Feature Shipped to Production (Mandatory)

Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.

POCs, internal demos, and one-off scripts do not qualify.

2 to 4 Years of Professional Software or AI Engineering Experience

With at least one production AI feature owned end to end.

Strong Python Proficiency and API Development with FastAPI

Comfort with type hints, async, packaging, testing, streaming responses, and authentication.

Production-grade Python, not notebook-only code.

Hands-on Depth Across the LLM and Agent Stack

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

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

Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).

Solutioning Speed and POC Velocity

Demonstrated ability to move from a fuzzy problem to a working prototype in days.

Strong instinct for what to build first, what to defer, and what to throw away.

Cost Discipline for Production AI

Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.

Treats unit economics as a first-class concern.

AWS Familiarity

Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.

Comfortable in a Fast-Moving Environment

Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.

Strong Written and Spoken English Communication

Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.

Nice to Have

• fine-tuning or LoRA, QLoRA, PEFT exposure
• MCP server authoring
• eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
• open-source AI contributions
• multi-modal models (vision, audio)

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.

Experience - 4 to 6 year

Location – Ahmedabad/Pune/Indore

• Additional Job Description

Additional Job Description

Required Skills and Experience: 

• Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
• Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.
• Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
• Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
• Demonstrated experience implementing content filtering / moderation systems.
• Solid skills working with structured and unstructured data and advanced feature engineering.
• Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
• Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
• Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
• Good knowledge of security, data governance, and privacy best practices for AI systems.

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 30 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Key Responsibilities  

• Design, build, and optimize scalable data pipelines for AI/ML applications.
• Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
• Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases.
• Fine-tune open-source and foundation models using domain-specific datasets.
• Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management.
• Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
• Develop APIs and AI services for production deployment.
• Collaborate with cross-functional teams to deliver scalable AI-driven solutions.
• Monitor model performance, troubleshoot production issues, and maintain technical documentation.

Required Skills  

Mandatory  

• 1–3 years of experience in Data Science, Data Engineering, or AI/ML development.
• Strong programming skills in Python and SQL.
• Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings.
• Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation.
• Experience in LLM fine-tuning and working with Hugging Face models.
• Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD.
• Experience with Git, REST APIs, Linux environments, and data processing libraries.

Preferred  

• Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate.
• Familiarity with Docker, Kubernetes, and MLflow.
• Exposure to Apache Spark or Airflow for data engineering workflows.
• Experience with cloud platforms (AWS, Azure, or GCP).

Primary Technology Stack  

• Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark
• AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn
• Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n
• Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings
• Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models
• Vector Databases: Pinecone, Chroma, Milvus, Weaviate
• Databases: PostgreSQL, MongoDB
• MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git
• Cloud Platforms: AWS, Azure, GCP

Experience: 1–3 Years

Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps

The Role

You own AI systems end to end. From the speech-to-text models that turn audio into text, to the diarization that separates and identifies speakers, to the agentic layer that turns conversation into memory and action, to the observability and evaluation that keep all of it honest in production. This is a wide role by design. You will own model selection, serving, and production reliability. If you want to tune one model and ignore the system around it, this is not the role.

What You Will Own

•     Speech-to-text. Evaluate, integrate, and optimize STT models across cloud and self-hosted. Drive accuracy and cost trade-offs with ground-truth metrics.

•     Speaker diarization and identification. Push accuracy on hard, real-world, multi-speaker audio.

•     Agentic AI. Build the memory and retrieval pipeline, LLM orchestration, and the agent workflows that sit on top of captured conversation.

•     Model serving and infrastructure. Stand up and optimize self-hosted serving (vLLM, Triton class). Own latency, throughput, and cost per user.

Observability

An always-on wearable means models run in production every second, on messy real-world audio. You own the visibility into that.

•     Instrument the full audio-to-memory pipeline: STT, diarization, retrieval, and LLM calls.

•     Define and track model-quality SLOs in production: transcription drift, diarization error over time, retrieval relevance, latency, throughput, and cost per user.

•     Build dashboards and alerting so model degradation is caught before users feel it.

•     Trace failures across a distributed, always-on system using metrics, logs, and traces.

•     Close the loop. Production signals feed back into evaluation and model selection.

Evaluation

We do not ship what we cannot measure. You own the systems that prove a model is actually better, not just newer.

•     Build and own ground-truth evaluation harnesses for every model in the stack.

•     Measure with real metrics: WER for transcription, DER for diarization, Recall and F1 for retrieval and speaker identification.

•     Build and maintain labeled benchmark datasets that reflect real, messy, multi-speaker audio.

•     Run regression and A/B evaluations on every model swap, prompt change, or pipeline update. Nothing ships on a vibe.

•     Reject anecdotal proxies, single confidence scores, and cherry-picked examples as evidence of quality.

What We Are Looking For

•     3 to 5 years as an AI/ML engineer with production systems behind you. Engineering and production experience is non-negotiable.

•     Depth across the modern AI stack: LLMs, speech models, vector retrieval, model serving.

•     Strong software engineering. You write code that ships and survives contact with real users.

•     Fluency in Python and the production ML ecosystem.

•     Comfort with cloud infrastructure (GCP a plus) and containerized deployment on Kubernetes.

•     A working command of observability and evaluation. You measure first and trust metrics over intuition.

•     First-principles reasoning and metric discipline.

Nice to Have

•     Research background or publications. A strong signal, not a substitute for production work.

•     Audio and speech ML experience (STT, diarization, voice).

•     Experience self-hosting and optimizing open models.

•     Experience with LLM gateway and agent orchestration patterns.

•     Experience building eval harnesses or production model-monitoring systems.

Requirements

Agentic work is must. Audio is good to have

. Self hosting models is a must

 Experience with LLM gateway and agent orchestration is a must have

About NonStop io Technologies

NonStop io Technologies is a value-driven company with a strong focus on process-oriented software engineering. We specialize in Product Development and have a decade's worth of experience in building web and mobile applications across various domains. NonStop io Technologies follows core principles that guide its operations and believes in staying invested in a product's vision for the long term. We are a small but proud group of individuals who believe in the 'givers gain' philosophy and strive to provide value in order to seek value. We are committed to and specialize in building cutting-edge technology products and serving as trusted technology partners for startups and enterprises. We pride ourselves on fostering innovation, learning, and community engagement. Join us to work on impactful projects in a collaborative and vibrant environment.

Brief Description:

We're seeking an AI/ML Engineer to join our team. As AI/ML Engineer, you will be responsible for designing, developing, and implementing artificial intelligence (AI) and machine learning (ML) solutions to solve real-world business problems. You will work closely with engineering teams, including software engineers, domain experts, and product managers, to deploy and integrate Applied AI/ML solutions into the products that are being built at NonStop io. Your role will involve researching cutting-edge algorithms and data processing techniques, and implementing scalable solutions to drive innovation and improve the overall user experience.

Responsibilities

● Applied AI/ML engineering; Building engineering solutions on top of the AI/ML tooling available in the industry today. Eg: Engineering APIs around OpenAI

● AI/ML Model Development: Design, develop, and implement machine learning models and algorithms that address specific business challenges, such as natural language processing, computer vision, recommendation systems, anomaly detection, etc.

● Data Preprocessing and Feature Engineering: Cleanse, preprocess, and transform raw data into suitable formats for training and testing AI/ML models. Perform feature engineering to extract relevant features from the data

● Model Training and Evaluation: Train and validate AI/ML models using diverse datasets to achieve optimal performance. Employ appropriate evaluation metrics to assess model accuracy, precision, recall, and other relevant metrics

● Data Visualization: Create clear and insightful data visualizations to aid in understanding data patterns, model behaviour, and performance metrics

● Deployment and Integration: Collaborate with software engineers and DevOps teams to deploy AI/ML models into production environments and integrate them into various applications and systems

● Data Security and Privacy: Ensure compliance with data privacy regulations and implement security measures to protect sensitive information used in AI/ML processes

● Continuous Learning: Stay updated with the latest advancements in AI/ML research, tools, and technologies, and apply them to improve existing models and develop novel solutions

● Documentation: Maintain detailed documentation of the AI/ML development process, including code, models, algorithms, and methodologies for easy understanding and future reference.

Qualifications & Skills

● Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, or a related field. Advanced degrees or certifications in AI/ML are a plus

● Proven experience as an AI/ML Engineer, Data Scientist, or related role, ideally with a strong portfolio of AI/ML projects

● Proficiency in programming languages commonly used for AI/ML. Preferably Python

● Familiarity with popular AI/ML libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, etc.

● Familiarity with popular AI/ML Models such as GPT3, GPT4, Llama2, BERT etc.

● Strong understanding of machine learning algorithms, statistics, and data structures

● Experience with data preprocessing, data wrangling, and feature engineering

● Knowledge of deep learning architectures, neural networks, and transfer learning

● Familiarity with cloud platforms and services (e.g., AWS, Azure, Google Cloud) for scalable AI/ML deployment

● Solid understanding of software engineering principles and best practices for writing maintainable and scalable code

● Excellent analytical and problem-solving skills, with the ability to think critically and propose innovative solutions

● Effective communication skills to collaborate with cross-functional teams and present complex technical concepts to non-technical stakeholders

AI/ML Engineer (Open-Source LLM – BFSI Domain)

Job Title: AI/ML Engineer (Open-Source LLM – BFSI Domain)

Location: Brookefield, Bengaluru

Employment Type: Full-Time

Work Schedule: Monday to Saturday (Alternate Saturdays Off)

Working Hours: 9:00 AM – 6:00 PM (Extendable based on project requirements)

About the Role

We are seeking a talented AI/ML Engineer with hands-on experience in building, fine-tuning, and deploying Open-Source Large Language Models (LLMs) for the Banking, Financial Services, and Insurance (BFSI) domain. The ideal candidate will have practical experience developing production-ready AI solutions and a passion for leveraging Generative AI to solve real-world business challenges.

In this role, you will collaborate with Product, Engineering, and Data Science teams to design intelligent AI solutions that enhance customer experience, automate business processes, and improve operational efficiency.

Key Responsibilities

Design, develop, fine-tune, and deploy Open-Source LLMs for BFSI use cases.

Build AI-powered applications using modern LLM frameworks and orchestration tools.

Collaborate with Product Managers, Data Scientists, and Software Engineers to understand business requirements and deliver scalable AI solutions.

Conduct model experimentation, evaluation, optimization, and performance benchmarking using real-world datasets.

Implement Retrieval-Augmented Generation (RAG), prompt engineering, vector databases, and AI workflows where applicable.

Monitor model performance in production environments and continuously improve model accuracy, latency, and scalability.

Ensure AI solutions comply with enterprise security, privacy, and regulatory standards.

Develop APIs and integrate AI models into enterprise applications.

Maintain technical documentation, architecture diagrams, and deployment procedures.

Stay updated with the latest advancements in Artificial Intelligence, Machine Learning, and Open-Source LLM technologies.

Required Qualifications

Education

Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a related technical field.

Equivalent practical experience will also be considered.

Experience

Minimum 2+ years of hands-on AI/ML development experience.

Experience in the BFSI domain is preferred.

Proven experience building and deploying AI/ML solutions in production environments.

Technical Skills

Programming Languages

Python (Mandatory)

Java (Preferred)

AI/ML Frameworks

PyTorch

TensorFlow

Scikit-learn

Open-Source LLM Technologies

Experience with one or more of the following:

LangChain

LangGraph

Ollama

Hugging Face Transformers

Llama

Mistral

DeepSeek

Qwen

vLLM

FastAPI

Generative AI

Prompt Engineering

Fine-Tuning LLMs

Retrieval-Augmented Generation (RAG)

Embeddings

Vector Databases (FAISS, ChromaDB, Milvus, Pinecone, etc.)

Model Evaluation and Optimization

Additional Skills

Docker

Kubernetes (Preferred)

REST APIs

Git

Linux

CI/CD pipelines

Core Competencies

Strong analytical and problem-solving skills.

Excellent understanding of AI/ML concepts and LLM architectures.

Ability to communicate technical concepts to non-technical stakeholders.

Strong interpersonal and collaboration skills.

Self-motivated with a passion for continuous learning and innovation.

Preferred Experience

Experience in any of the following areas will be an added advantage:

Banking & Financial Services applications

Fraud Detection

Credit Risk Assessment

Intelligent Document Processing

Loan Processing Automation

Customer Support Chatbots

Regulatory Compliance

OCR & Document AI

Agentic AI and Multi-Agent Systems

Interview Process

Round 1 – Technical Interview

Conducted by: Senior AI Engineer

Assessment includes:

Python Programming

Machine Learning Fundamentals

Open-Source LLMs

LangChain & RAG

Coding and Problem Sol…