Microsoft Dynamics 365 Developer

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)

1. Write high-quality technical content, blogs, website content, and articles
2. Create engaging social media content for different platforms
3. Research industry trends, competitors, and audience interests
4. Optimize content using SEO best practices
5. Work closely with the marketing and design team for content planning
6. Ensure all content is clear, thoughtful, and aligned with the brand voice
7. Proofread and edit content before publishing
8. Take ownership of assigned projects and meet deadlines consistently

About Us: Website: https://www.tundratechnical.ca/ & https://tundramanagedsolutions.com/

Experience: 5-8 years

Shift: EST

Location: Bangalore

Mode: Hybrid

Position Requirements

Core (Must-Have) Skills

• Proficiency in Python with advanced use of NumPy, Pandas, and Scikit-learn

• Expertise in SQL, including complex queries and performance optimization

• Strong foundation in machine learning techniques (regression, classification, clustering, ensemble methods)

• Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI

• MLOps practices (MLFlow, Kubeflow, CI/CD pipelines for ML workflows)

• Experience in data visualization (Seaborn, Plotly, Tableau, or Power BI)

• Applied knowledge of statistics and probability for model development

• Hands-on experience with cloud-based ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI)

• Exposure to large-scale data processing (Spark, Hadoop)

Preferred Skills

• Deep learning frameworks (TensorFlow, PyTorch)

• Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI

• MLOps practices (MLFlow, Kubeflow, CI/CD pipelines for ML workflows)

• Data pipeline orchestration (Airflow, Prefect)

• Business domain knowledge (e.g., retail, finance)

Key Activities

• Preparing and preprocessing structured and unstructured data for analysis

• Designing, training, and validating machine learning models

• Deploying and operationalizing models in collaboration with engineering teams

• Building dashboards and data-driven visual insights for stakeholders

• Partnering with product and engineering teams to align models with business needs

• Monitoring, optimizing, and retraining models for performance improvements

8-16 years of overall database administration and support/operations experience, with a successful delivery track record.
# Proficient knowledge in  MySQL, HANA administration.
#Should have knowledge in setting up DR and HA approaches
#Should have strong Knowledge in Backup & Recovery.
# Should have played a L2 role in current position
# Experience of working in large landscape/environment where multiple database flavors are running.
# Knowledge on Database capacity planning/strategy is an added advantage
# Knowledge & Experience on other database as HANA flavors are added advantage
# Hands on experience with expert knowledge of Unix/Linux flavors w.r.t Database.
# Good understanding of operational framework like ITIL/ITSM.
# Should be capable of Owning, Leading and coordinate operational tasks, customer escalations, process improvements.
# Excellent presentation & communication skills are a must.

Title 

ML Product Manager

Reporting to 

Sr. Director of Product Management

About the role

We are looking for a Product Manager for our AI/ML Product team that is responsible for delivering ML powered products and applications such as Campaign recommendations and personalization.

As part of the team, the candidate will lead the full product lifecycle of the product, including market research, competitive analysis, gathering requirements, developing specifications, and getting the product developed and delivered to customers. 

You will help transform ideas into AI products for our customers and drive adoption working with cross functional teams.

What You'll Do

• Own product feature from market research, execution, go-to-market and impact evaluation.
• Review customer feedback, analyze impact and prioritize it, and convert into clearly defined product requirements to create a feature backlog
• Write product specs, define product UI with the UX team, review test plans.
• Collaborate with Engineering in product development, validating builds and driving releases to meet target dates.
• Be the product champion. Document product functionality and train the organization on new features. Define the value proposition of your products and guide marketing / sales in selling your products. 
• Facilitate in data-driven decision making and create the telemetry and metrics required to evaluate the success of the products you launch.
• Define and drive internal and external pilots prior to releases to gather early feedback and ensure adoption of new features and functionality. 

What You'll Need

• 4+ years of Product Management experience in an enterprise B2B/B2B2C SaaS company building products in one or more of the following domains: marketing cloud products, such as Campaign Management, Segmentation, Marketing Automation
• Experience working on AI/ML projects and applications, Data & Analytics
• Experience working in Agile development environment  
• Strong analytical and problem-solving skills
• Strong communication skills 
• Self-starter, detail-oriented
• Technical background required. MBA is a strong plus.

Bonus Points if You Have

• Familiarity with multi-cloud environments
• Familiarity with Enterprise Data Platforms, CDP and Enterprise Integrations 

About Telyport

Telyport is an emerging tech startup in the logistics space. We are building a robust hyperlocal distribution network that is affordable, convenient, and reliable for locals, SMEs, and large enterprises with a vision to bring local commerce online.

Job Description: Core Member of TELYPORT TECHNOLOGY TEAM

We are looking for a Sr. iOS developer responsible for the development and maintenance of applications aimed at a range of iOS devices including mobile phones and tablet computers. Your primary focus will be development of iOS applications and their integration with back-end services. You will be working alongside other engineers and developers working on different layers of the infrastructure. Therefore, a commitment to collaborative problem solving, sophisticated design, and the creation of quality products is essential.

We never really thought that a college degree was the thing that you had to have to do well. We trust in team’s deliverables, smart & the hard work.

Responsibilities: Build & Maintain TELYPORT’s iOS STACK

- Design and build applications for the iOS platform.

- Ensure the performance, quality, and responsiveness of applications.

- Collaborate with a team to define, design, and ship new features.

- Identify and correct bottlenecks and fix bugs.

- Help maintain code quality, organization, and automatization.

Skills: LOVES iOS & APPLE’s WAY OF HANDLING THINGS

- Proficient with Objective-C or Swift (depending on project requirements), and Cocoa Touch.

- Experience with iOS frameworks such as Core Data, Core Animation, etc.

- Experience with offline storage, threading, and performance tuning.

- Familiarity with RESTful APIs to connect iOS applications to back-end services.

- Knowledge of other web technologies and UI/UX standards.

- Understanding of Apple’s design principles and interface guidelines.

- Knowledge of low-level C-based libraries is preferred.

- Experience with performance and memory tuning with tools.

- Familiarity with cloud message APIs and push notifications.

- Knack for benchmarking and optimization.

- Proficient understanding of code versioning tools. (such as Git, Mercurial or SVN)

- Familiarity with continuous integration.

This is not your typical 9-5 job. If you are a hustler, passionate about what you do and would like to challenge yourself into new areas and arenas, look no further. You will have absolute creative independence in executing your roles while at the same time be chasing aggressive goals and targets to pursue and a highly competitive group of people to challenge and learn from.