Key Responsibilities Design and build LLM guardrails for prompt injection protection, toxicity/bias detection, and hallucination/jailbreak identification. Build and maintain evaluation frameworks to monitor LLM safety, fairness, and compliance. Develop automated pipelines to process, tag, and evaluate LLM outputs using Python and SQL. Leverage vector databases and embeddings to detect unsafe content or model drift. Create internal dashboards and visualizations (Streamlit, Dash, or lightweight React/JS) for POCs and internal tools. Collaborate with ML engineers and product teams to integrate LLM safety components into production APIs or applications. Stay current with AI safety research, emerging tools (Ragas, LangChain, Guardrails.ai), and regulatory standards (EU AI Act, NIST AI RMF). Required Qualifications 3 - 4.5 years of experience in data science, applied ML, or LLM-based applications. Strong programming skills in Python and experience writing SQL for data exploration or feature engineering. Solid understanding of NLP, deep learning (CNN/RNN/Transformers), and LLM architectures. Hands-on experience with Hugging Face, LangChain, LLM APIs (OpenAI, Anthropic), and vector stores (FAISS, Pinecone, Chroma). Familiarity with front-end basics (Streamlit, Dash, or simple HTML/CSS/JS) is a plus , not mandatory. Experience with model evaluation, red‑teaming, or safety interventions in NLP/LLM systems. (Preferred) Familiarity with deploying ML pipelines in production (Docker, FastAPI) and ability to thrive in a fast‑paced startup environment.
Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall. Responsibilities Design, build, and optimize Python‑based APIs using Flask or FastAPI. Develop high‑performance microservices to process and route LLM prompts securely and efficiently. Handle high‑traffic, low‑latency workloads , applying profiling and optimization techniques. Integrate with internal tools and third‑party APIs to enforce safety, security, and compliance rules. Write clean, scalable, and well‑tested code following best engineering practices. Containerize and deploy services using Docker and, optionally, Kubernetes. Collaborate closely with AI/ML and security teams to implement guardrails across LLM workflows. Qualifications 3–5 years of professional experience in backend development with a strong focus on Python Proven experience with Flask or FastAPI , with a solid understanding of API design, versioning, and documentation best practices. Proficiency in Pandas for rapid data inspection and manipulation. Comfortable working with Docker ; exposure to Kubernetes is a plus. A performance‑focused mindset with attention to latency, throughput, and concurrency.
The Opportunity As enterprise digital landscape is expanding everyday, but they don't know how to secure it. We solve this problem with cutting-edge Cybersecurity solutions powered with AI. As our Chief Revenue Officer & Co-Founder , you'll own everything related to revenue: Find customers Sell our product Close deals Grow the business You'll be the first sales person and eventually build a team around you. What You'll Do: Find and Win Customers Research and identify potential enterprise customers Cold email, LinkedIn outreach, and phone calls Demo our product to CISOs and security teams Run pilot programs and proof-of-concepts Negotiate contracts and close deals Build the Sales Foundation Create our sales process from scratch Set pricing and positioning strategy Track all sales metrics and pipeline Collect customer feedback to improve our product Grow the Business Target: Close ₹3 crores in deals within 18 months Onboard customers successfully Identify opportunities to sell more to existing customers Eventually hire and lead a sales team What We're Looking For Experience 5-10 years selling B2B software to enterprises You've closed deals worth ₹50+ lakhs each Experience selling to security teams or IT leaders is a plus You understand complex technical products Skills & Mindset Hunter mentality: You love finding new customers and making cold calls Results-driven: You track numbers and hit your targets Technical: You can understand and explain our AI security platform Startup mindset: You're comfortable with uncertainty and building from zero Leadership: Ready to eventually hire and manage a sales team
Role Overview You’ll turn technical security features into clear stories that customers understand and sales teams can use. You’ll support launches, create enablement content, improve website messaging, and help the company win more deals with security and IT buyers. Responsibilities • Write crisp messaging, value propositions, and proofs for core use cases and buyer personas • Create sales enablement assets: one-pagers, pitch decks, FAQs, objection handling notes, competitive battlecards • Draft website and landing page copy; collaborate on SEO topics and internal linking • Support GTM for feature launches: naming, positioning, packaging basics, and simple pricing narratives • Research competitors and adjacent categories; maintain a living matrix of differentiators and evidence • Partner with Product to create mini-briefs that define problem, audience, benefits, and success criteria • Produce short case studies, blog posts, demo talk tracks, and webinar outlines • Gather insights from customer calls, win/loss notes, and analytics to refine messaging LinkedIn (organic): Build a weekly content calendar; write posts for company and exec handles; repurpose blogs, releases, wins, and customer quotes into short posts and carousels; monitor comments/DMs and surface leads. LinkedIn (paid, basic): Draft ad copy, choose angles/offers, set audiences with Campaign Manager, brief creatives, track CTR/CPL via UTMs, and summarize learnings. Qualifications 1–2 years in B2B product marketing/content/presales for tech or cybersecurity, with a portfolio of 3+ assets. Sharp writing that converts positioning, headlines, CTAs plus landing pages, emails, and LinkedIn posts/carousels; solid UTM and basic GA4/LinkedIn analytics. Product promotion chops: plan and ship mini-campaigns end-to-end; coordinate with Sales/Demand Gen; track CTR, demo requests, and influenced pipeline. Sales enablement builder: pitch decks, one-pagers, battlecards, objection FAQs; distill competitive research into clear differentiators. Cybersecurity fluency: explain IAM, data protection/DLP, endpoint/cloud, detection/response, and compliance basics; turn customer/SE feedback into tighter messaging. Competitive research : turn product differences into clear customer benefits and a living differentiator matrix. Tool comfort : CRM (HubSpot/Salesforce), CMS (Webflow/WordPress), docs (Notion/Google Docs), basic Figma/Canva for light edits. Bonus: Basic graphic design skills (Figma/Canva/Adobe) to create LinkedIn carousels, infographics, one-pagers, and simple video thumbnails while maintaining brand style and exporting web-ready assets. What you'll Get : Own meaningful outcomes from day one with direct access to founders and rapid feedback. Clear growth path with a defined thirty sixty ninety plan, mentorship, and the chance to own a product area within six to twelve months. Competitive package with salary, ESOPs, remote first setup on India hours, learning budget, and modern tools. Build a public portfolio through one pagers, case studies, launches, and LinkedIn campaigns that showcase your impact.
Company Description Nyuway is a premier Cyber Security solutions provider specializing in Application Security. The company offers innovative, AI-enabled, and comprehensive application security products and services to help businesses secure their digital presence, ensure regulatory compliance, and build resilience against emerging threats. Role Description This is a full-time remote role for a Junior Product Manager at Nyuway. The Junior Product Manager will be responsible for day-to-day tasks related to product development, market research, feature prioritization, and collaborating with cross-functional teams to ensure successful product launches and enhancements. Responsibilities Own a product slice end-to-end define vision, strategy, and quarterly OKRs; keep a living roadmap aligned to business goals. Author production-ready specs crisp PRDs, flows, and acceptance criteria; partner with design for prototypes; get early sign-off from Eng/Sec/GTM. Run lightweight discovery: user interviews, problem statements, JTBD, workflow mapping. plan releases, manage scope, unblock dependencies, and drive outcomes; instrument feature flags, kill switches, and rollback paths. Write PRDs, user stories, acceptance criteria; maintain a clear, prioritized backlog. Partner with engineering/design to scope releases, manage sprints, and unblock delivery. Define success metrics, set up simple dashboards, and run post-release reviews. Support QA/UAT, reproduce issues, track defects to closure. Contribute to launch assets: release notes, one-pagers, internal enablement. Track competitive landscape and synthesize customer feedback into the roadmap. produce one-pagers, release notes, battlecards, and demo scripts; equip sales/CS with clear “who/why/when” for each launch. Qualifications 0–2 years in product/BA/PM internship or junior role (security, AI, or developer tools is a plus). Product Development, Market Research, and Feature Prioritization skills Experience in collaborating with cross-functional teams Excellent interpersonal and communication skills Analytical and problem-solving abilities Knowledge of Cyber Security and Application Security concepts Bachelor's degree in Computer Science, Engineering, Business, or related field Strong written communication; can turn ambiguity into a one-page brief and PRD. Analytical basics: funnels, cohorts, A/B test principles; proficiency with spreadsheets.
Key Responsibilities Design and build LLM guardrails for prompt injection protection, toxicity/bias detection, and hallucination/jailbreak identification. Build and maintain evaluation frameworks to monitor LLM safety, fairness, and compliance. Develop automated pipelines to process, tag, and evaluate LLM outputs using Python and SQL. Leverage vector databases and embeddings to detect unsafe content or model drift. Create internal dashboards and visualizations (Streamlit, Dash, or lightweight React/JS) for POCs and internal tools. Collaborate with ML engineers and product teams to integrate LLM safety components into production APIs or applications. Stay current with AI safety research, emerging tools (Ragas, LangChain, Guardrails.ai), and regulatory standards (EU AI Act, NIST AI RMF). Required Qualifications 1-3 years of experience in data science, applied ML, or LLM-based applications. Strong programming skills in Python and experience writing SQL for data exploration or feature engineering. Solid understanding of NLP, deep learning (CNN/RNN/Transformers), and LLM architectures. Hands-on experience with Hugging Face, LangChain, LLM APIs (OpenAI, Anthropic), and vector stores (FAISS, Pinecone, Chroma). Familiarity with front-end basics (Streamlit, Dash, or simple HTML/CSS/JS) is a plus , not mandatory. Experience with model evaluation, red‑teaming, or safety interventions in NLP/LLM systems. (Preferred) Familiarity with deploying ML pipelines in production (Docker, FastAPI) and ability to thrive in a fast‑paced startup environment.