Kasba Peth Pune Maharashtra Ai developer Jobs – 1330 Open Jobs Today

AI Developer
Genetrix Technology —Pune, Maharashtra
- ₹12,00,000 - ₹30,00,000 a year
- Full-time
- Salesforce
- Master's degree
- Databases
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IT Shastra —Pune, Maharashtra
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Citi —Pune, Maharashtra
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iProgrammer —Pune, Maharashtra
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BMC Software —Pune, Maharashtra
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AutomationEdge —Pune, Maharashtra
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Vizlitics Technologies Private Limited —Pune, Maharashtra
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- Full-time
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Quick apply
1d
AI Developer
nCircle Tech —Pune District, Maharashtra
- Full-time
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Persistent —Pune, Maharashtra
- Full-time
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AssetSoft —Pune, Maharashtra
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The Toro Company —Pune, Maharashtra
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GenAI Developer
Barclays —Pune, Maharashtra
- DevOps
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AWS Connect + Claude
Kiash Solutions LLP —Pune, Maharashtra
- ₹24,00,000 - ₹28,00,000 a year
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Quick apply
3d
AI Solution Designer
Nihilent —Pune, Maharashtra
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Gephels —Pune, Maharashtra
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FIS Global —Pune, Maharashtra
- Full-time
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Deloitte —Pune, Maharashtra
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AI Developer

Genetrix Technology
Pune, Maharashtra

Apply Now

Job details

Full-time
₹12,00,000 - ₹30,00,000 a year
14 days ago

Qualifications

TensorFlow
CI/CD
Salesforce Marketing Cloud
Image processing
Cloud infrastructure
Law
Kubernetes
PyTorch
Computer vision
Enterprise Software
Salesforce
Master's degree
Databases
Docker
Machine learning
Continuous integration
APIs
AI
Graph databases
Python

Full job description

Machine Learning

Developer

AI Developer

Engineering

Full-time

Hybrid – Pune

Posted on 18 August 2026

2+ years

About Genetrix

Genetrix Technology is a boutique Salesforce consulting firm and ISV partner headquartered in Pune, India, with a US entity in Dover, Delaware. We specialize in Marketing Cloud, Data Cloud and Agentforce, supporting clients and Salesforce partners across the US, UK and APAC through direct and white-label delivery.

We combine consulting depth with product innovation. Our portfolio includes JourneyVoice, Campaign Clarity, Marketing Cloud Translator (MCT) and LinkLect. Our founder, Aman Batra, was recognized as a Salesforce Marketing Champion in 2021.

What you will own

Problem framing and experimentation. Turn open-ended business or product questions into testable hypotheses, prototypes, success criteria and an evidence-led build plan.
AI system architecture. Design applications and agents that combine models, retrieval, memory, tools, workflows and human hand-offs with clear boundaries and failure paths.
Data and retrieval. Build reliable ingestion, transformation, indexing and retrieval pipelines; reason carefully about context quality, permissions, freshness and grounding.
Applied model development. Select and adapt the right approach across LLMs, classical machine learning and relevant multimodal methods rather than forcing every problem into one pattern.
Evaluation and safety. Create representative datasets and evaluation loops for response quality, task completion, tool use, latency, cost, robustness and risk; add guardrails where the use case demands them.
Production engineering. Build maintainable services and APIs; contribute to deployment, monitoring, observability, versioning, incident diagnosis and continuous improvement.
Integration and product ownership. Connect AI capabilities with enterprise systems and Genetrix products, communicate trade-offs clearly and stay accountable from discovery through adoption.

What success looks like

You move beyond the prototype. Promising experiments become reliable capabilities that users can understand, adopt and trust.
You make quality visible. Claims about performance are supported by thoughtful evaluation, production signals and honest analysis, not selective examples.
You choose the right trade-offs. You balance model quality, latency, cost, security, maintainability and user experience according to the problem.
You create reusable leverage. The patterns, components and learning you produce make the next intelligent product faster and stronger to build.
You connect engineering to value. You can explain what the system changes for a customer, user or business and use that understanding to guide technical decisions.

What we value

Clarity: AI work is full of uncertainty. You communicate assumptions, evidence, trade-offs and limitations in a way that helps technical and non-technical people make good decisions.
Curiosity: You go beneath the surface of a model, dataset or user request. You test what could be true, examine why something failed and keep looking for the insight that creates leverage.
Sincerity: You do not disguise uncertainty or overstate a result. You are honest about what you know, surface risks early and protect the trust placed in you by users and teammates.
Learning and business thinking: The tooling will keep changing. You learn quickly without chasing novelty for its own sake, and you ask whether the system solves the right problem and creates durable value.

What you bring

Demonstrable production experience. At least 2 years of proper hands-on experience building AI/ML or AI-powered software, with deployed work, working demos, code, case studies or other evidence you can discuss in depth.
Strong software foundations. Fluency in Python and sound engineering practices across APIs, testing, debugging, data structures, version control and maintainable service design.
Modern AI depth. Practical experience with LLM applications, retrieval-augmented generation, embeddings, tool use, structured outputs, agents or model adaptation, and the judgment to know when simpler methods are better.
Machine-learning judgment. Understanding of data quality, training and validation, metrics, error analysis, overfitting, experimentation and the limits of a model.
Production awareness. Experience with databases, cloud infrastructure, containers, CI/CD, monitoring or MLOps practices sufficient to carry a system beyond a notebook.
Product and communication ability. You can clarify a vague need, explain a technical choice, collaborate across functions and stay focused on user and business outcomes.

We require at least 2 years of relevant experience, but tenure alone will not decide the outcome. We will look closely at what you personally built, how deeply you understand it, how it behaved outside a controlled demo and what decisions you would improve now.

Helpful, but not automatic gatekeepers

Frameworks and model ecosystems. Experience with PyTorch, TensorFlow, Hugging Face, LangGraph, LlamaIndex or comparable tools, used with an understanding of the abstractions underneath.
Evaluation and operations. Familiarity with experiment tracking, model or prompt versioning, tracing, observability, MLflow, Weights & Biases, Docker, Kubernetes or cloud AI platforms.
Specialized AI experience. Depth in computer vision, speech, recommendation, forecasting, optimization, knowledge graphs or fine-tuning is valuable when supported by real problem-solving experience.
Enterprise and responsible AI. Experience with permissions, privacy, security, human review, adversarial testing or integrations with systems such as Salesforce.

Level and compensation

The expected CTC range for this role is INR 12-30 LPA. Final compensation will be determined based on your skillset, depth of expertise, relevant experience, interview performance and the scope of responsibility discussed during the process.

This is a Pune-based hybrid role. Candidates must be open to relocating to Pune to be considered.

Before you decide to join us

A two-way decision. It is part of our culture that, before you sign an offer letter, we invite you to meet the entire team and spend time with us in our Pune office. If you are based in another city, we fly you to Pune so you can experience the environment, ask candid questions and decide whether the fit feels right. We want you to make this decision with context, not just from an interview call.

How to apply

Apply through this listing with your CV or LinkedIn profile. Include links to one or two working projects, demos, repositories or case studies. Tell us what you personally owned, the architecture and evaluation choices you made, what failed, and what outcome the system created.

If your path is unconventional or you do not match every tool listed, we would still like to hear from you if your work demonstrates depth, strong fundamentals and the learning mindset described above.

Apply Now

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Machine Learning

Developer

AI Developer

About Genetrix

What you will own

What success looks like

What we value

What you bring

Helpful, but not automatic gatekeepers

Level and compensation

Before you decide to join us

How to apply