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**Who we are**

Mindtickle is the market-leading revenue productivity platform that combines on-the-job learning and deal execution to get more revenue per rep. Mindtickle is recognized as a market leader by top industry analysts and is ranked by G2 as the #1 sales onboarding and training product. We’re honoured to be recognized as a Leader in the first-ever Forrester Wave: Revenue Enablement Platforms, Q3 2024

As an SDE-3 in AI/ML, you will:

- Translate business asks and requirements into technical requirements, solutions, architectures, and implementations.
- Define clear problem statements and technical requirements by aligning business goals with AI research objectives.
- Lead the end-to-end design, prototyping, and implementation of AI systems, ensuring they meet performance, scalability, and reliability targets.
- Architect solutions for GenAI and LLM integrations, including prompt engineering, context management, and agentic workflows.
- Develop and maintain production-grade code with high test coverage and robust CI/CD pipelines on AWS, Kubernetes, and cloud-native infrastructures.
- Establish and maintain post-deployment monitoring, performance testing, and alerting frameworks to ensure performance and quality SLAs are met.
- Conduct thorough design and code reviews, uphold best practices, and drive technical excellence across the team.
- Mentor and guide junior engineers and interns, fostering a culture of continuous learning and innovation.
- Collaborate closely with product management, QA, data engineering, DevOps, and customer facing teams to deliver cohesive AI-powered product features.

**Key Responsibilities**:
- Problem Definition & Requirements- 1.Translate business use cases into detailed AI/ML problem statements and success metrics.- 2.Gather and document functional and non-functional requirements, ensuring traceability throughout the development lifecycle.
- Architecture & Prototyping- 1.Design end-to-end architectures for GenAI and LLM solutions, including context orchestration, memory modules, and tool integrations.- 2.Build rapid prototypes to validate feasibility, iterate on model choices, and benchmark different frameworks and vendors.
- Development & Productionization- 1.Write clean, maintainable code in Python, Java, or Go, following software engineering best practices.- 2.Implement automated testing (unit, integration, and performance tests) and CI/CD pipelines for seamless deployments.- 3.Optimize model inference performance and scale services using containerization (Docker) and orchestration (Kubernetes).
- Post-Deployment Monitoring- 1.Define and implement monitoring dashboards and alerting for model drift, latency, and throughput.- 2.Conduct regular performance tuning and cost analysis to maintain operational efficiency.
- Mentorship & Collaboration- 1.Mentor SDE-1/SDE-2 engineers and interns, providing technical guidance and career development support.- 2.Lead design discussions, pair-programming sessions, and brown-bag talks on emerging AI/ML topics.- 3.Work cross-functionally with product, QA, data engineering, and DevOps to align on delivery timelines and quality goals.

**Required Qualification**:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 8+ years of professional software development experience, with at least 3 years focused on AI/ML systems.
- Strong programming skills in Python and one or more of Java, Go, or C++.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerized deployments.
- Deep understanding of machine learning algorithms, LLM architectures, and prompt engineering.
- Expertise in CI/CD, automated testing frameworks, and MLOps best practices.
- Excellent written and verbal communication skills, with the ability to distill complex AI concepts for diverse audiences.

**Preferred Experience**:
- Prior experience building Agentic AI or multi-step workflow systems (using tools like Langgrah, CrewAI or similar).
- Familiarity with open-source LLMs (e.g., Hugging Face hosted) and custom fine-tuning.
- Familiarity with ASR (Speech to Text) and TTS (Text to Speech), and other multi-modal systems.
- Experience with monitoring and observability tools (e.g. Datadog, Prometheus, Grafana).
- Publications or patents in AI/ML or related conference presentations.
- Knowledge of GenAI evaluation frameworks (e.g., Weights & Biases, CometML).
- Proven experience designing, implementing, and rigorously testing AI-driven voice agents - integrating with platforms such as Google Dialogflow, Amazon Lex, and Twilio Autopilot - and ensuring high performance and reliability.

**What we offer?**:
- Opportunity to work at the forefront of GenAI, LLMs, and Agentic AI in a fast-growing SaaS environment.
- Collaborative, inclusive culture focused on innovation, continuous learning, and professional growth.
- Competitive compensation, comprehensive benefits, and equity options.
- Flexible work arrangements and support