Job
Description
About Mindtickle’s AI/ML Engineering Team Mindtickle is a revenue productivity solution that helps revenue teams enhance their performance by identifying areas for improvement for each team member, recommending appropriate remedial actions, and providing opportunities to implement those recommendations. The charter of the CoE-ML team is to enhance Mindtickle’s solution offerings-such as embedding artificial intelligence in the form of CoPilots, developing hyper-realistic AI-powered role plays, and enabling the automatic curation of collateral while also improving Mindtickle’s internal operations. This includes optimizing workflows, accelerating business processes, and making information more easily discoverable. We work on cutting-edge technologies to drive innovation and deliver advanced AI solutions. We maintain high-quality evaluation standards and continuous improvement practices to ensure our AI features meet stringent performance and reliability criteria. Role Overview 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 Translate business use cases into detailed AI/ML problem statements and success metrics. Gather and document functional and non-functional requirements, ensuring traceability throughout the development lifecycle. Architecture & Prototyping Design end-to-end architectures for GenAI and LLM solutions, including context orchestration, memory modules, and tool integrations. Build rapid prototypes to validate feasibility, iterate on model choices, and benchmark different frameworks and vendors. Development & Productionization Write clean, maintainable code in Python, Java, or Go, following software engineering best practices. Implement automated testing (unit, integration, and performance tests) and CI/CD pipelines for seamless deployments. Optimize model inference performance and scale services using containerization (Docker) and orchestration (Kubernetes). Post-Deployment Monitoring Define and implement monitoring dashboards and alerting for model drift, latency, and throughput. Conduct regular performance tuning and cost analysis to maintain operational efficiency. Mentorship & Collaboration Mentor SDE-1/SDE-2 engineers and interns, providing technical guidance and career development support. Lead design discussions, pair-programming sessions, and brown-bag talks on emerging AI/ML topics. Work cross-functionally with product, QA, data engineering, and DevOps to align on delivery timelines and quality goals. Required Qualifications 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. Proven track record of architecting and deploying production AI applications at scale. 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 for professional development. Show more Show less
