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Job Title: Machine Operator

Location : Kalyan Nashik highway

Qualification: 12th Pass / ITI / Diploma

Experience: 0–1 Year / Fresher

Salary: ₹15,000 – ₹20,000 per month

Job Description

• Set up and operate production machines as per standard operating procedures.
• Monitor machine performance to ensure smooth operations and quality output.
• Make basic machine adjustments when required and report technical issues to the supervisor.
• Perform routine inspection, cleaning, and minor maintenance of machines.
• Inspect finished products to ensure quality standards are met.
• Maintain production records and update daily logs accurately.
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Skills Required

• Basic knowledge of machine operations and production processes
• Understanding of safety procedures in manufacturing
• Attention to detail and quality checking ability
• Basic mechanical aptitude
• Ability to follow instructions and work in shifts
• Teamwork and discipline

Relationship Manager- Broking

Key Responsibilities:

·         Build and maintain strong relationships with Authorized Partners (APs).

·         Drive business growth and enhance partner revenue.

·         Provide training, mentoring, and activation support for business partners.

·         Ensure compliance with regulatory and procedural standards.

·         Promote stock broking products such as Smallcase, Stock SIP, MTF, Investments & Trading.

·         Engage, motivate, and develop business partnerships.

·         Deliver service excellence and meet key business KPIs.

Requirements:

·         Minimum 1 year of experience in broking/financial markets.

·         Strong communication & relationship management skills.

·         Good knowledge of financial markets & trading.

·         Comfortable working in a target-driven environment.

About the role:

Unico Connect is an Al-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies. We are hiring a Senior Al Engineer for a dedicated client engagement focused on building an Al-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 LLMpowered 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 singleagent vs. multi-agent designs, including planner/executor splits and dedicated buildrepair 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 qualitydegrading changes from shipping. Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time. This responsibility is nonnegotiable 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 Al work, and raise the engineering bar across the Al 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 Al system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost. POCs, internal demos, and tutorialgrade work do not qualify.

5+ years of professional software or Al engineering experience, with at least 3 years focused on LLM applications, Al engineering, or production Al 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 OpenAl, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together). Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, Llamalndex 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 Al. Track record of measurable cost optimisation on production Al 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 Al 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 Al contributions; published technical writing on LLM systems; multi-modal model experience; fine-tuning exposure (LORA, QLORA, PEFT).

Strong investor management profile

Mandatory (Experience): Must have 4+ years of fundraising experience startups, AIFs, or investment products), investor management and network building

Mandatory (Tech Skills 1): Must be proficient in pitching investment opportunities and onboarding investors

Mandatory (Tech Skills 2 ): Must have experience conducting investor meetings & presentations

Mandatory (Tech Skills 3): Must have experience achieving revenue targets

Mandatory (Tech Skills 4): Must have experience representing the company at networking events

Mandatory (Tech Skills 5): Must understand startup valuation basics, portfolio reporting, investment products.

Mandatory (Tech Skills 6): Must prepare investment proposals, performance reports, and KPIs.

Mandatory (Education): No strict requirement, but finance background preferred.

Mandatory (Note): Must have high ownership and target-driven mindset.

Preferred

Preferred (Company): Preferably from VC firms, AIFs, Wealth Management, Investment Banking, or startup fundraising roles.

Preferred (Industry): Events industry

Klimb.io was founded by an ISB Alumni who has been solving hiring problems since 2012 and has worked with Naukri.com, Shaadi.com, and Expedia.com etc. Our foundation is based on his extensive experience and domain knowledge in Technology, Software Product Management & Recruiting.

We are seeking a detail-oriented and proactive Accounts Executive responsible for managing day-to-day accounting operations, statutory compliance, payroll, financial reporting, and administrative coordination. The ideal candidate with solid understanding of GST, TDS, and financial reporting, along with the ability to streamline administrative processes and ensure timely execution of recurring and annual financial obligations.

Company link: https://www.catalystmi.com/" target="_blank">https://www.catalystmi.com/

Job Location: Mumbai (Hybrid)

• Expertise in full stack tools Angular, SpringBoot, Node JS 
• Expertise on RDBMS and no SQL databases like Oracle, MS SQL, MySQL, Mongo DB 
• Understanding of microservices architecture 
• Good understanding of Agile practices and DevOps tools like Jenkins/Azure etc. 
• Zeal to drive solutions and results with a constant focus on business priorities and delivering business value. 
• Ability to learn new tools and technologies quickly and extend the knowledge with other team members. 
• Basic understanding of AI/ML and R / Python 
• Good written and verbal communication. 

ML Engineer-Analyst/ Senior Analyst

Job purpose:

To design and develop machine learning and deep learning systems. Run machine learning tests andexperiments and implementing appropriate ML algorithms. Works cross-functionally with the Data Scientists, Software application developers and business groups for the development of innovative ML models. Use Agile experience to work collaboratively with other Managers/Owners in geographically distributed teams.

Accountabilities:

• Work with Data Scientists and Business Analysts to frame problems in a business context. Assist all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production.

• Understand business objectives and developing models that help to achieve them, along with metrics to track their progress.

• Explore and visualize data to gain an understanding of it, then identify differences in data distribution that could affect performance when deploying the model in the real world.

• Define validation strategies, preprocess or feature engineering to be done on a given dataset and data augmentation pipelines.

• Analyze the errors of the model and design strategies to overcome them.
• Collaborate with data engineers to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production and demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.

Qualifications & Specifications

• Bachelor's degree in Engineering /Computer Science/ Math/ Statistics or equivalent. Master's degree in relevant specification will be first preference

• Experience of machine learning algorithms and libraries
• Understanding of data structures, data modeling and software architecture.
• Deep knowledge of math, probability, statistics and algorithms
• Experience with machine learning platforms such as Microsoft Azure, Google Cloud, IBM Watson, and Amazon

• Big data environment: Hadoop, Spark
• Programming languages: Python, R, PySpark
• Supervised & Unsupervised machine learning: linear regression, logistic regression, k-means

clustering, ensemble models, random forest, svm, gradient boosting

• Sampling data: bagging & boosting, bootstrapping
• Neural networks: ANN, CNN, RNN related topics
• Deep learning: Keras, Tensorflow
• Experience with AWS Sagemaker deployment and agile methodology

• Web :  Angular2+ (min 3 years), HTML5, NodeJS, Yarn
• Programming: Typescript
• IDE Tools : Visual Studio Code,
• Source Control – Good knowledge on SVN/Git
• Good to have: Java8, SpringBoot, JPA, Oracle PL SQL, SOAP, Restful, WSDL, ETL tools like Talend
  
  Role Summary:
• Software Development with best coding practices
• Proper co-ordination with other team members for maintaining source code versioning
• Complete and provide status of work to Team Lead/Project Manager regularly
• Create and maintain proper documents related to development work
• Collaborate with other team to create quicker issue resolution to achieve high customer satisfaction
• Flexible to learn other technologies and work across teams
• Clear understanding of requirement from functional team
• Involvement in analysis, design, coding, SIT, UAT and support